19.4.3 Super Resolution or Superresolution, General

Yoo, J.S.[Jun-Sang], Kim, J.O.[Jong-Ok],
Noise-Robust Iterative Back-Projection,
IP(29), No. , 2020, pp. 1219-1232.
IEEE DOI 1911
Image reconstruction, Noise measurement, Principal component analysis, Image resolution, Noise robustness, cost optimization BibRef

Catala, P.[Paul], Duval, V.[Vincent], Peyré, G.[Gabriel],
A Low-Rank Approach to Off-the-Grid Sparse Superresolution,
SIIMS(12), No. 3, 2019, pp. 1464-1500.
DOI Link 1911
BibRef

Yang, X.M.[Xiao-Mei], Zhang, J.W.[Jia-Wei], Liu, Y.[Yanan], Zheng, X.J.[Xiu-Juan], Liu, K.[Kai],
Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters,
VC(35), No. 12, December 2018, pp. 1755-1768.
WWW Link. 1912
BibRef

Wang, D.Y.[Dao-Yong], Yang, X.M.[Xiao-Min], Pu, Q.[Qin], Jeon, G.G.[Gwang-Gil], Liu, K.[Kai],
PSAM: Progressive Spatial Adaptive Matching for Reference-Based Super Resolution,
SPLetters(30), 2023, pp. 1717-1721.
IEEE DOI 2312
BibRef

Bhandari, A.[Ayush], Conde, M.H.[Miguel Heredia], Loffeld, O.[Otmar],
One-Bit Time-Resolved Imaging,
PAMI(42), No. 7, July 2020, pp. 1630-1641.
IEEE DOI 2006
Imaging, Time measurement, Image resolution, Sensors, Quantization (signal), Current measurement, Photonics, sparse recovery and time-resolved imaging BibRef

Li, F., Bai, H., Zhao, Y.,
FilterNet: Adaptive Information Filtering Network for Accurate and Fast Image Super-Resolution,
CirSysVideo(30), No. 6, June 2020, pp. 1511-1523.
IEEE DOI 2006
Image reconstruction, Image resolution, Convolution, Information filters, Convolutional neural networks, Training, adaptive information fusion BibRef

Köhler, T.[Thomas], Bätz, M.[Michel], Naderi, F.[Farzad], Kaup, A.[André], Maier, A.[Andreas], Riess, C.[Christian],
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data,
PAMI(42), No. 11, November 2020, pp. 2944-2959.
IEEE DOI 2010
Benchmark testing, Databases, Spatial resolution, Observers, Cameras, Hardware, Super-resolution, ground truth, simulated-to-real gap, observer study BibRef

Nayak, R.[Rajashree], Patra, D.[Dipti], Balabantaray, B.K.[Bunil Ku],
Super-resolution image reconstruction using molecular docking,
IET-IPR(14), No. 12, October 2020, pp. 2922-2936.
DOI Link 2010
BibRef

Qiao, H.,
A Universal Technique for Analysing Discrete Super-Resolution Algorithms,
SPLetters(27), 2020, pp. 1829-1833.
IEEE DOI 2011
Signal resolution, Image resolution, Signal processing algorithms, Eigenvalues and eigenfunctions, dantzig selector BibRef

Yang, Y.[Yue], Qi, Y.[Yong],
Image super-resolution via channel attention and spatial graph convolutional network,
PR(112), 2021, pp. 107798.
Elsevier DOI 2102
Image super-resolution, Graph convolutional, Adjacent matrix BibRef

Zhang, X.Y.[Xin-Yan], Gao, P.[Peng], Liu, S.X.Y.[Sun-Xiang-Yu], Zhao, K.Y.[Kong-Ya], Li, G.T.[Gui-Tao], Yin, L.G.[Liu-Guo], Chen, C.W.[Chang Wen],
Accurate and Efficient Image Super-Resolution via Global-Local Adjusting Dense Network,
MultMed(23), 2021, pp. 1924-1937.
IEEE DOI 2107
Feature extraction, Image reconstruction, Computational modeling, Task analysis, Computational efficiency, Data mining, refinement structure BibRef

Chow, Y.T.[Yat Tin], Deng, Y.J.[You-Jun], He, Y.Z.[You-Zi], Liu, H.Y.[Hong-Yu], Wang, X.C.[Xian-Chao],
Surface-Localized Transmission Eigenstates, Super-resolution Imaging, and Pseudo Surface Plasmon Modes,
SIIMS(14), No. 3, 2021, pp. 946-975.
DOI Link 2108
BibRef

Prajapati, K.[Kalpesh], Chudasama, V.[Vishal], Upla, K.[Kishor], Raia, K.[Kiran], Ramachandra, R.[Raghavendra], Busch, C.[Christoph],
Channel Split Convolutional Neural Network for Single Image Super-Resolution (CSISR),
FG21(1-8)
IEEE DOI 2303
Performance evaluation, Training, Image synthesis, Computational modeling, Superresolution, Memory management, Computational efficiency BibRef

Prajapati, K.[Kalpesh], Chudasama, V.[Vishal], Patel, H.[Heena], Sarvaiya, A.[Anjali], Upla, K.[Kishor], Raja, K.[Kiran], Ramachandra, R.[Raghavendra], Busch, C.[Christoph],
Channel Split Convolutional Neural Network (ChaSNet) for Thermal Image Super-Resolution,
PBVS21(4363-4372)
IEEE DOI 2109
BibRef
Earlier: A2, A3, A1, A5, A7, A6, A8, Only:
TherISuRNet: A Computationally Efficient Thermal Image Super-Resolution Network,
PBVS20(388-397)
IEEE DOI 2008
Image sensors, Visualization, Thermal factors, Superresolution, Thermal sensors, Optical imaging, Thermal noise. Cameras, Spatial resolution, Feature extraction, Training, Computer architecture BibRef

Pu, X.F.[Xiao-Feng], Wang, Z.M.[Zeng-Mao],
Multistage reaction-diffusion equation network for image super-resolution,
IET-IPR(15), No. 12, 2021, pp. 2926-2936.
DOI Link 2109
BibRef

Mehta, N.[Nancy], Murala, S.[Subrahmanyam],
MSAR-Net: Multi-scale attention based light-weight image super-resolution,
PRL(151), 2021, pp. 215-221.
Elsevier DOI 2110
Multi-scale attention residual block, Up and down-sampling projection block, Image super-resolution BibRef

Tan, Y.[Yang], Zheng, H.T.[Hai-Tian], Zhu, Y.H.[Yin-Heng], Yuan, X.Y.[Xiao-Yun], Lin, X.[Xing], Brady, D.[David], Fang, L.[Lu],
CrossNet++: Cross-Scale Large-Parallax Warping for Reference-Based Super-Resolution,
PAMI(43), No. 12, December 2021, pp. 4291-4305.
IEEE DOI 2112
Cameras, Spatial resolution, Signal resolution, Superresolution, Light fields, Training data, Photography, Noise reduction, Decoding, optical flow BibRef

Zheng, H.T.[Hai-Tian], Ji, M.Q.[Meng-Qi], Wang, H.Q.[Hao-Qian], Liu, Y.B.[Ye-Bin], Fang, L.[Lu],
CrossNet: An End-to-End Reference-Based Super Resolution Network Using Cross-Scale Warping,
ECCV18(VI: 87-104).
Springer DOI 1810
BibRef

Xu, X.Y.[Xiang-Yu], Ma, Y.R.[Yong-Rui], Sun, W.X.[Wen-Xiu], Yang, M.H.[Ming-Hsuan],
Exploiting Raw Images for Real-Scene Super-Resolution,
PAMI(44), No. 4, April 2022, pp. 1905-1921.
IEEE DOI 2203
BibRef
Earlier: A1, A2, A3, Only:
Towards Real Scene Super-Resolution With Raw Images,
CVPR19(1723-1731).
IEEE DOI 2002
Image color analysis, Cameras, Image restoration, Color, Pipelines, Data models, Super-resolution, raw image, training data generation, convolutional neural network (CNN) BibRef

Jiang, J.[Jie], Liu, J.[Jing], Fu, J.[Jun], Wang, W.N.[Wei-Ning], Lu, H.Q.[Han-Qing],
Super-Resolution Semantic Segmentation with Relation Calibrating Network,
PR(124), 2022, pp. 108501.
Elsevier DOI 2203
Image semantic segmentation, Super-resolution semantic segmentation, Relation calibrating BibRef

Song, L.[Li], Ge, Z.[Zhou], Lam, E.Y.[Edmund Y.],
Dual Alternating Direction Method of Multipliers for Inverse Imaging,
IP(31), 2022, pp. 3295-3308.
IEEE DOI 2205
Imaging, Convex functions, Optimization, Superresolution, Convergence, Minimization, Linear programming, Inverse imaging, image super-resolution BibRef

Bhandari, A.[Ayush],
Back in the US-SR: Unlimited Sampling and Sparse Super-Resolution With Its Hardware Validation,
SPLetters(29), 2022, pp. 1047-1051.
IEEE DOI 2205
Hardware, Imaging, Superresolution, Signal resolution, Sensors, Kernel, Heuristic algorithms, Analog-to-digital, modulo sampling, super-resolution BibRef

Zheng, Y.P.[Yan-Ping], Zeng, G.[Guang], Li, H.[Haisheng], Cai, Q.[Qiang], Du, J.P.[Jun-Ping],
Colorful 3D reconstruction at high resolution using multi-view representation,
JVCIR(85), 2022, pp. 103486.
Elsevier DOI 2205
3D reconstruction, Colorful volumes, Super resolution, Multi-view representation BibRef

Mylonopoulos, D.[Dario], Cascarano, P.[Pasquale], Calatroni, L.[Luca], Piccolomini, E.L.[Elena Loli],
Constrained and Unconstrained Inverse Potts Modelling for Joint Image Super-Resolution and Segmentation,
IPOL(12), 2022, pp. 92-110.
DOI Link 2205
Code, Superresolution.
See also Joint super-resolution and segmentation from a set of low resolution images using a Bayesian approach with a Gauss-Markov-Potts prior. BibRef

Ma, C.[Cheng], Yu, P.Q.[Pei-Qi], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Recovering Realistic Details for Magnification-Arbitrary Image Super-Resolution,
IP(31), 2022, pp. 3669-3683.
IEEE DOI 2206
Superresolution, Image edge detection, Signal resolution, Training, Spatial resolution, Optimization, photo-realistic BibRef

He, Z.Y.[Zong-Yao], Jin, Z.[Zhi], Zhao, Y.[Yao],
SRDRL: A Blind Super-Resolution Framework With Degradation Reconstruction Loss,
MultMed(24), 2022, pp. 2877-2889.
IEEE DOI 2206
Degradation, Kernel, Image reconstruction, Feature extraction, Noise level, Mathematical model, Training, multiple degradations BibRef

Wu, H.J.[Hong-Jun], Qi, H.R.[Hao-Ran], Zhang, H.R.[Huan-Rong], Jin, Z.[Zhi], Salihu, D.[Driton], Hu, J.F.[Jian-Fang],
Reconstruction with robustness: A semantic prior guided face super-resolution framework for multiple degradations,
IVC(140), 2023, pp. 104857.
Elsevier DOI 2312
Face super-resolution, Robustness, Multiple degradations, Semantic prior BibRef

Duan, P.Q.[Pei-Qi], Wang, Z.W.[Zihao W.], Shi, B.X.[Bo-Xin], Cossairt, O.[Oliver], Huang, T.J.[Tie-Jun], Katsaggelos, A.K.[Aggelos K.],
Guided Event Filtering: Synergy Between Intensity Images and Neuromorphic Events for High Performance Imaging,
PAMI(44), No. 11, November 2022, pp. 8261-8275.
IEEE DOI 2210
Sensors, Optical sensors, Spatial resolution, Optical imaging, High-speed optical techniques, Image reconstruction, joint filtering BibRef

Ma, C.[Cheng], Rao, Y.M.[Yong-Ming], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Structure-Preserving Image Super-Resolution,
PAMI(44), No. 11, November 2022, pp. 7898-7911.
IEEE DOI 2210
Feature extraction, Image edge detection, Superresolution, Distortion, Task analysis, Generative adversarial networks, generative adversarial network BibRef

Zhou, B.J.[Bi-Jun], Yan, H.B.[Hui-Bin], Wang, S.Y.[Shuo-Yao],
Structure and Texture Preserving Network for Real-World Image Super-Resolution,
SPLetters(29), 2022, pp. 2173-2177.
IEEE DOI 2212
Degradation, Tensors, Image reconstruction, Superresolution, Task analysis, Periodic structures, Image restoration, generative adversarial network BibRef

Cheng, D.Q.[De-Qiang], Chen, L.L.[Liang-Liang], Lv, C.[Chen], Guo, L.[Lin], Kou, Q.Q.[Qi-Qi],
Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution,
CirSysVideo(32), No. 12, December 2022, pp. 8436-8449.
IEEE DOI 2212
Lighting, Image reconstruction, Image enhancement, Robustness, Interference, Estimation, Superresolution, Image super-resolution, cross-fusion BibRef

Wang, S.[Shuang], Sun, Z.X.[Zheng-Xing], Li, Q.[Qian],
Image super-resolution based on self-similarity generative adversarial networks,
IET-IPR(17), No. 1, 2023, pp. 157-165.
DOI Link 2301
BibRef

Nguyen, N.L.[Ngoc-Long],
A Brief Analysis of the SwinIR Image Super-Resolution,
IPOL(12), 2022, pp. 582-589.
DOI Link 2301

See also SwinIR: Image Restoration Using Swin Transformer. BibRef

Li, H.[Hui], Zhang, K.B.[Kai-Bing], Niu, Z.X.[Zhen-Xing], Shi, H.Y.[Hong-Yu],
C^2MT: A Credible and Class-Aware Multi-Task Transformer for SR-IQA,
SPLetters(29), 2022, pp. 2662-2666.
IEEE DOI 2301
Super resolution, Quality Awareness. Redible and Class-Aware Multi-Task Transformer. Transformers, Task analysis, Prediction algorithms, Signal processing algorithms, Head, Multitasking, Training, patch-wise pseudo label generation BibRef

Saharia, C.[Chitwan], Ho, J.[Jonathan], Chan, W.[William], Salimans, T.[Tim], Fleet, D.J.[David J.], Norouzi, M.[Mohammad],
Image Super-Resolution via Iterative Refinement,
PAMI(45), No. 4, April 2023, pp. 4713-4726.
IEEE DOI 2303
Noise reduction, Superresolution, Task analysis, Iterative methods, Data models, Faces, Diffusion processes, Image super-resolution, deep generative models BibRef

Dahl, R., Norouzi, M., Shlens, J.,
Pixel Recursive Super Resolution,
ICCV17(5449-5458)
IEEE DOI 1802
image reconstruction, image resolution, given low resolution image, high magnification factors, Training BibRef

Chi, Y.C.[Yi-Chen], Yang, W.M.[Wen-Ming], Tian, Y.[Yapeng],
GDSSR: Toward Real-World Ultra-High-Resolution Image Super-Resolution,
SPLetters(30), 2023, pp. 95-99.
IEEE DOI 2303
Degradation, Image restoration, Training, Feature extraction, Superresolution, Visualization, Measurement, generative adversarial network BibRef

Zhang, P.[Ping], Zhang, Y.C.[Yong-Chao], Mao, D.Q.[De-Qing], Yan, J.A.[Jian-An], Liu, S.D.[Shuai-Di],
Fast Resolution Enhancement for Real Beam Mapping Using the Parallel Iterative Deconvolution Method,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
real beam mapping imagery. BibRef

Hockmann, M.[Mathias], Kunis, S.[Stefan],
Short Communication: Weak Sparse Superresolution is Well-Conditioned,
SIIMS(16), No. 1, 2023, pp. SC1-SC13.
DOI Link 2303
BibRef

Shao, D.G.[Dang-Guo], Qin, L.[Li], Xiang, Y.[Yan], Ma, L.[Lei], Xu, H.[Hui],
Medical image blind super-resolution based on improved degradation process,
IET-IPR(17), No. 5, 2023, pp. 1615-1625.
DOI Link 2304
image reconstruction, medical image processing, visual perception BibRef

Jiang, X.W.[Xin-Wei], Yang, J.[Jie], Ma, L.[Lei], Yang, Y.P.[Yi-Ping],
Multi-task Gaussian Process Regression-based Image Super Resolution,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Jiang, Y.M.[Yu-Ming], Chan, K.C.K.[Kelvin C.K.], Wang, X.T.[Xin-Tao], Loy, C.C.[Chen Change], Liu, Z.W.[Zi-Wei],
Reference-Based Image and Video Super-Resolution via C^2-Matching,
PAMI(45), No. 7, July 2023, pp. 8874-8887.
IEEE DOI 2306
BibRef
Earlier:
Robust Reference-based Super-Resolution via C2-Matching,
CVPR21(2103-2112)
IEEE DOI 2111
Superresolution, Feature extraction, Task analysis, Correlation, Fuses, Bridges, Image super-resolution, video super-resolution. Knowledge engineering, Visualization, Impedance matching, Benchmark testing, Robustness BibRef

Hong, J.H.[Jong-Hwan], Lee, B.[Bokyeung], Ko, K.[Kyungdeuk], Ko, H.S.[Han-Seok],
Fast Non-Local Attention network for light super-resolution,
JVCIR(95), 2023, pp. 103861.
Elsevier DOI 2309
Single Image Super-Resolution, Non-Local Attention, Light model BibRef

Deng, X.[Xin], Wang, H.[Hao], Xu, M.[Mai], Li, L.[Li], Wang, Z.[Zulin],
Omnidirectional Image Super-Resolution via Latitude Adaptive Network,
MultMed(25), 2023, pp. 4108-4120.
IEEE DOI 2310
BibRef

Deng, X.[Xin], Wang, H.[Hao], Xu, M.[Mai], Guo, Y.C.[Yi-Chen], Song, Y.H.[Yu-Hang], Yang, L.[Li],
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution,
CVPR21(9185-9194)
IEEE DOI 2111
Image segmentation, Adaptive systems, Laplace equations, Superresolution, Network architecture BibRef

Katz, R.[Rami], Diab, N.[Nuha], Batenkov, D.[Dmitry],
Decimated Prony's Method for Stable Super-Resolution,
SPLetters(30), 2023, pp. 1467-1471.
IEEE DOI 2310
BibRef

Luo, Z.X.[Zheng-Xiong], Huang, Y.[Yan], Li, S.[Shang], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
End-to-End Alternating Optimization for Real-World Blind Super Resolution,
IJCV(131), No. 12, December 2023, pp. 3152-3169.
Springer DOI 2311
BibRef

Shu, R.[Rui], Zhao, C.R.[Cai-Rong], Feng, S.Y.[Shu-Yang], Zhu, L.[Liang], Miao, D.Q.[Duo-Qian],
Text-Enhanced Scene Image Super-Resolution via Stroke Mask and Orthogonal Attention,
CirSysVideo(33), No. 11, November 2023, pp. 6317-6330.
IEEE DOI 2311
BibRef

Hao, Y.K.[Yu-Kun], Yu, F.H.[Fei-Hong],
Super-Resolution Degradation Model: Converting High-Resolution Datasets to Optical Zoom Datasets,
CirSysVideo(33), No. 11, November 2023, pp. 6374-6389.
IEEE DOI 2311
BibRef

Fei, Z.[Zetao], Zhang, H.[Hai],
IFF: A Superresolution Algorithm for Multiple Measurements,
SIIMS(16), No. 4, 2023, pp. 2175-2201.
DOI Link 2312
BibRef

Wang, H.[Hang], Ding, Z.Y.[Zhen-Yu], Cheng, C.[Cheng], Li, Y.[Yuhai], Sun, H.B.[Hong-Bin],
MDC-Net: Multi-domain constrained kernel estimation network for blind image super resolution,
CVIU(238), 2024, pp. 103865.
Elsevier DOI 2312
Blind super-resolution, Deep learning, Degradation kernel estimation, Multi-domain constraints BibRef

Chen, Q.P.[Qiang-Pu], Qin, J.H.[Jing-Hui], Wen, W.S.[Wu-Shao],
ALAN: Self-Attention Is Not All You Need for Image Super-Resolution,
SPLetters(31), 2024, pp. 11-15.
IEEE DOI 2401
BibRef

Zhu, L.Z.[Lin-Zhen], Wu, R.J.[Ren-Jie], Lee, B.G.[Boon-Giin], Nkenyereye, L.[Lionel], Chung, W.Y.[Wan-Young], Xu, G.[Gen],
FEGAN: A Feature-Oriented Enhanced GAN for Enhancing Thermal Image Super-Resolution,
SPLetters(31), 2024, pp. 541-545.
IEEE DOI 2402
Training, Feature extraction, Iron, Image reconstruction, Image edge detection, Imaging, Measurement, thermal imaging BibRef

Xiong, Q.M.[Qi-Ming], Gao, Z.R.[Zhi-Rong], Ma, J.Y.[Jia-Yi], Ma, Y.[Yong],
Multi-image super-resolution based low complexity deep network for image compressive sensing reconstruction,
JVCIR(99), 2024, pp. 104071.
Elsevier DOI 2403
Compressive sensing reconstruction, Deep learning, Grouping initial reconstruction, Image super-resolution, Low complexity BibRef

Zhou, X.Q.[Xiao-Qiang], Huang, H.B.[Huai-Bo], Wang, Z.[Zilei], He, R.[Ran],
RISTRA: Recursive Image Super-Resolution Transformer With Relativistic Assessment,
MultMed(26), 2024, pp. 6475-6487.
IEEE DOI 2404
Transformers, Image restoration, Superresolution, Feature extraction, Task analysis, Quality assessment, parameter sharing BibRef

Li, X.[Xiyao], Zhao, X.Q.[Xiao-Qiang],
MCFPN: Multi-Path Cross Fusion Pyramid-Like Network for Image Super-Resolution Reconstruction,
SPLetters(31), 2024, pp. 2265-2269.
IEEE DOI 2410
Feature extraction, Image reconstruction, Superresolution, Convolution, Training, Data mining, Remote sensing, super-resolution reconstruction BibRef

Lee, H.[Hongjae], Yoo, J.S.[Jun-Sang], Jung, S.W.[Seung-Won],
RefQSR: Reference-Based Quantization for Image Super-Resolution Networks,
IP(33), 2024, pp. 2823-2834.
IEEE DOI 2405
Quantization (signal), Superresolution, Computational efficiency, Task analysis, Image reconstruction, Upper bound, Limiting, reference-based quantization BibRef

Zhao, Y.Q.[Yao-Qian], Teng, Q.Z.[Qi-Zhi], Chen, H.G.[Hong-Gang], Zhang, S.J.[Shu-Jiang], He, X.H.[Xiao-Hai], Li, Y.[Yi], Sheriff, R.E.[Ray E.],
Activating More Information in Arbitrary-Scale Image Super-Resolution,
MultMed(26), 2024, pp. 7946-7961.
IEEE DOI 2405
Feature extraction, Image reconstruction, Task analysis, Adaptation models, Kernel, Superresolution, Information filters, deformable convolution BibRef

Zhang, D.C.[Da-Cheng], Zhang, W.[Wei], Lei, W.M.[Wei-Min], Chen, X.[Xinyi],
Diverse branch feature refinement network for efficient multi-scale super-resolution,
IET-IPR(18), No. 6, 2024, pp. 1475-1490.
DOI Link 2405
convolutional neural nets, image enhancement, image processing, image resolution, image restoration BibRef

Yang, C.[Cheng], Lu, G.M.[Guan-Ming],
Unsupervised Image Blind Super Resolution via Real Degradation Feature Learning,
IET-CV(18), No. 4, 2024, pp. 485-498.
DOI Link 2406
convolutional neural nets, feature extraction, image processing, image reconstruction, image restoration, neural nets BibRef

Xu, Y.M.[Yi-Min], Gao, N.X.[Nan-Xi], Chao, F.[Fei], Ji, R.R.[Rong-Rong],
An efficient blur kernel estimation method for blind image Super-Resolution,
PR(154), 2024, pp. 110590.
Elsevier DOI Code:
WWW Link. 2406
Blind super-resolution reconstruction, Efficient inference, Kernel detection and reconstruction BibRef

Chen, G.Y.[Guang-Yong], Weng, W.D.[Wu-Ding], Su, J.N.[Jian-Nan], Gan, M.[Min], Chen, C.L.P.[C. L. Philip],
Dynamic Degradation Intensity Estimation for Adaptive Blind Super-Resolution: A Novel Approach and Benchmark Dataset,
CirSysVideo(34), No. 6, June 2024, pp. 4762-4772.
IEEE DOI 2406
Degradation, Estimation, Adaptation models, Kernel, Data models, Training, Image reconstruction, Blind super-resolution, benchmark dataset BibRef

Chen, X.H.[Xiao-Hui], Chen, L.[Lin], Chen, L.J.[Ling-Jun], Chen, P.[Peng], Sheng, G.Q.[Guan-Qun], Yu, X.S.[Xiao-Sheng], Zou, Y.B.[Yao-Bin],
Modeling Thermal Infrared Image Degradation and Real-World Super-Resolution Under Background Thermal Noise and Streak Interference,
CirSysVideo(34), No. 7, July 2024, pp. 6194-6206.
IEEE DOI 2407
Degradation, Thermal noise, Interference, Feature extraction, Superresolution, Image reconstruction, Thermal degradation, streak interference BibRef

Sun, L.C.[Ling-Chen], Liang, J.[Jie], Liu, S.Z.[Shuai-Zheng], Yong, H.W.[Hong-Wei], Zhang, L.[Lei],
Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective,
IP(33), 2024, pp. 4444-4458.
IEEE DOI Code:
WWW Link. 2408
BibRef

Xu, T.S.[Tian-Shuo], Li, L.J.[Li-Jiang], Mi, P.[Peng], Zheng, X.[Xiawu], Chao, F.[Fei], Ji, R.R.[Rong-Rong], Tian, Y.H.[Yong-Hong], Shen, Q.[Qiang],
Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization,
PAMI(46), No. 9, September 2024, pp. 6199-6215.
IEEE DOI 2408
Entropy, Superresolution, Self-supervised learning, Distortion, Analytical models, Optimization, Image restoration, information entropy BibRef

Lin, H.[Hai], Yang, J.[JunJie],
Image super-resolution reconstruction based on implicit image functions,
IET-IPR(18), No. 10, 2024, pp. 2690-2701.
DOI Link 2408
convolutional neural nets, image reconstruction, multilayer perceptrons BibRef

Guan, W.X.[Wen-Xue], Li, H.[Haobo], Xu, D.W.[Da-Wei], Liu, J.X.[Jia-Xin], Gong, S.H.[Sheng-Hua], Liu, J.[Jun],
Frequency Generation for Real-World Image Super-Resolution,
CirSysVideo(34), No. 8, August 2024, pp. 7029-7040.
IEEE DOI 2408
Feature extraction, Superresolution, Degradation, Convolution, Circuits and systems, Kernel, Image reconstruction, adaptive feature fusion BibRef

Ma, Z.C.[Zhi-Cheng], Liu, Z.X.[Zhao-Xiang], Wang, K.[Kai], Lian, S.[Shiguo],
Hybrid attention transformer with re-parameterized large kernel convolution for image super-resolution,
IVC(149), 2024, pp. 105162.
Elsevier DOI 2408
Image super-resolution, Transformer, Hybrid attention, Large kernel convolution, Re-parameterization BibRef

Liu, Y.H.[Yu-Hao], Chu, Z.Z.[Zhen-Zhong], Wei, L.F.[Li-Fei],
A Channel Contrastive Attention-Based Local-Nonlocal Mutual Block on Super-Resolution,
IEICE(E108-D), No. 9, September 2024, pp. 1219-1227.
WWW Link. 2410
BibRef

Wang, B.Y.[Bo-Yang], Liu, B.[Bowen], Liu, S.Y.[Shi-Yu], Yang, F.Y.[Feng-Yu],
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data,
WACV24(4290-4300)
IEEE DOI Code:
WWW Link. 2404
Degradation, Image coding, Superresolution, Pipelines, Noise, Neural networks, Video compression, Algorithms BibRef

Kazerouni, A.[Amirhossein], Azad, R.[Reza], Hosseini, A.[Alireza], Merhof, D.[Dorit], Bagci, U.[Ulas],
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings,
WACV24(1287-1296)
IEEE DOI 2404
Knowledge engineering, Shape, Superresolution, Noise reduction, Noise, Robustness, Algorithms, Image recognition and understanding BibRef

Daultani, D.[Dinesh], Larochelle, H.[Hugo],
Consolidating Separate Degradations Model via Weights Fusion and Distillation,
VAQuality24(440-449)
IEEE DOI Code:
WWW Link. 2404
Degradation, Training, Computational modeling, Semantic segmentation, Noise, Superresolution BibRef

Zhang, L.[Lin], Li, X.[Xin], He, D.L.[Dong-Liang], Li, F.[Fu], Ding, E.[Errui], Zhang, Z.X.[Zhao-Xiang],
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution,
ICCV23(13072-13081)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yin, Z.[Zhicun], Liu, M.[Ming], Li, X.M.[Xiao-Ming], Yang, H.[Hui], Xiao, L.[Longan], Zuo, W.M.[Wang-Meng],
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces,
ICCV23(12987-12998)
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, A.[Ao], Zhang, L.[Le], Liu, Y.[Yun], Zhu, C.[Ce],
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution,
ICCV23(12480-12490)
IEEE DOI Code:
WWW Link. 2401
BibRef

Chen, Z.[Zheng], Zhang, Y.[Yulun], Gu, J.J.[Jin-Jin], Kong, L.[Linghe], Yang, X.K.[Xiao-Kang], Yu, F.[Fisher],
Dual Aggregation Transformer for Image Super-Resolution,
ICCV23(12278-12287)
IEEE DOI Code:
WWW Link. 2401
BibRef

An, H.Y.[Hong-Yu], Zhang, X.F.[Xin-Feng],
Perception-Oriented Omnidirectional Image Super-Resolution Based on Transformer Network,
ICIP23(3583-3587)
IEEE DOI 2312
BibRef

Jena, S.[Swastik], Panda, S.[Saptarshi], Balabantaray, B.K.[Bunil Kumar], Nayak, R.[Rajashree],
Uncertainty Aware Implicit Image Function for Arbitrary-Scale Super-Resolution,
ICIP23(2440-2444)
IEEE DOI 2312
BibRef

Liu, Z.S.[Zhi-Song], Wang, Z.[Zijia], Jia, Z.[Zhen],
Soft-IntroVAE for Continuous Latent Space Image Super-Resolution,
ICIP23(1460-1464)
IEEE DOI 2312
BibRef

Sun, X.P.[Xiao-Peng], Li, W.Q.[Wei-Qi], Zhang, Z.Y.[Zhen-Yu], Ma, Q.[Qiufang], Sheng, X.[Xuhan], Cheng, M.[Ming], Ma, H.Y.[Hao-Yu], Zhao, S.J.[Shi-Jie], Zhang, J.[Jian], Li, J.L.[Jun-Lin], Zhang, L.[Li],
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution,
NTIRE23(1293-1301)
IEEE DOI 2309
BibRef

Kasliwal, A.[Aditya], Seth, P.[Pratinav], Rallabandi, S.[Sriya], Singhal, S.[Sanchit],
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-Resolution,
PBVS23(507-514)
IEEE DOI 2309
BibRef

Liu, T.[Tao], Cheng, J.[Jun], Tan, S.[Shan],
Spectral Bayesian Uncertainty for Image Super-Resolution,
CVPR23(18166-18175)
IEEE DOI 2309
BibRef

Pak, B.[Byeonghyun], Lee, J.W.[Jae-Won], Jin, K.H.[Kyong Hwan],
B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution,
CVPR23(10062-10071)
IEEE DOI 2309
BibRef

Gao, S.C.[Si-Cheng], Liu, X.[Xuhui], Zeng, B.[Bohan], Xu, S.[Sheng], Li, Y.J.[Yag-Jing], Luo, X.Y.[Xiao-Yan], Liu, J.Z.[Jian-Zhuang], Zhen, X.T.[Xian-Tong], Zhang, B.C.[Bao-Chang],
Implicit Diffusion Models for Continuous Super-Resolution,
CVPR23(10021-10030)
IEEE DOI 2309
BibRef

Grosche, S.[Simon], Regensky, A.[Andy], Seiler, J.[Jürgen], Kaup, A.[André],
Image Super-Resolution Using T-Tetromino Pixels,
CVPR23(9989-9998)
IEEE DOI 2309
BibRef

Chen, D.[Du], Liang, J.[Jie], Zhang, X.D.[Xin-Dong], Liu, M.[Ming], Zeng, H.[Hui], Zhang, L.[Lei],
Human Guided Ground-Truth Generation for Realistic Image Super-Resolution,
CVPR23(14082-14091)
IEEE DOI 2309
BibRef

Zou, H.[Han], Xu, L.[Liang], Okatani, T.[Takayuki],
Geometry Enhanced Reference-based Image Super-resolution,
IMW23(6124-6133)
IEEE DOI 2309
BibRef

Li, Y.[Yawei], Zhang, K.[Kai], Liang, J.Y.[Jing-Yun], Cao, J.[Jiezhang], Liu, C.[Ce], Gong, R.[Rui], Zhang, Y.[Yulun], Tang, H.[Hao], Liu, Y.[Yun], Demandolx, D.[Denis], Ranjan, R.[Rakesh], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
LSDIR: A Large Scale Dataset for Image Restoration,
NTIRE23(1775-1787)
IEEE DOI 2309
BibRef

Wang, Y.[Yue], Ming, J.[Jiawen], Jia, X.[Xu], Elder, J.H.[James H.], Lu, H.C.[Hu-Chuan],
Blind Image Super-Resolution with Degradation-aware Adaptation,
ACCV22(III:69-85).
Springer DOI 2307
BibRef

Zhuge, Y.Z.[Yun-Zhi], Jia, X.[Xu],
Multi-granularity Transformer for Image Super-Resolution,
ACCV22(III:138-154).
Springer DOI 2307
BibRef

Li, L.[Lei], Tang, J.Z.[Jing-Zhu], Chen, M.[Ming], Zhao, S.J.[Shi-Jie], Li, J.L.[Jun-Lin], Zhang, L.[Li],
Multi-patch Learning: Looking More Pixels in the Training Phase,
AIM22(549-560).
Springer DOI 2304
Compressed image super-resolution. BibRef

Lian, W.[Wenyi], Lian, W.J.[Wen-Jing],
Sliding Window Recurrent Network for Efficient Video Super-resolution,
AIM22(591-601).
Springer DOI 2304
BibRef

Yue, S.J.[Shi-Jie], Li, C.H.[Cheng-Hua], Zhuge, Z.Y.[Zheng-Yang], Song, R.X.[Rui-Xia],
Eesrnet: A Network for Energy Efficient Super-resolution,
AIM22(602-618).
Springer DOI 2304
BibRef

Luo, Z.W.[Zi-Wei], Li, Y.[Youwei], Yu, L.[Lei], Wu, Q.[Qi], Wen, Z.H.[Zhi-Hong], Fan, H.Q.[Hao-Qiang], Liu, S.C.[Shuai-Cheng],
Fast Nearest Convolution for Real-time Efficient Image Super-resolution,
AIM22(561-572).
Springer DOI 2304
BibRef

Zhou, L.[Lin], Cai, H.M.[Hao-Ming], Gu, J.J.[Jin-Jin], Li, Z.[Zheyuan], Liu, Y.Q.[Ying-Qi], Chen, X.Y.[Xiang-Yu], Qiao, Y.[Yu], Dong, C.[Chao],
Efficient Image Super-resolution Using Vast-Receptive-Field Attention,
AIM22(256-272).
Springer DOI 2304
BibRef

Gao, S.[Si], Zheng, C.J.[Cheng-Jian], Zhang, X.F.[Xiao-Feng], Liu, S.L.[Shao-Li], Wu, B.[Biao], Lu, K.D.[Kai-Di], Zhang, D.[Diankai], Wang, N.[Ning],
RCBSR: Re-parameterization Convolution Block for Super-resolution,
AIM22(540-548).
Springer DOI 2304
BibRef

Qin, X.R.[Xiao-Ran], Zhu, Y.[Yu], Li, C.H.[Cheng-Hua], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
Cidbnet: A Consecutively-interactive Dual-branch Network for Jpeg Compressed Image Super-resolution,
AIM22(458-474).
Springer DOI 2304
BibRef

Mahapatra, D.[Dwarikanath], Bozorgtabar, B.[Behzad], Reyes, M.[Mauricio],
Medical Image Super Resolution by Preserving Interpretable and Disentangled Features,
MIA-COVID19D22(709-721).
Springer DOI 2304
BibRef

Georgescu, M.I.[Mariana-Iuliana], Ionescu, R.T.[Radu Tudor], Miron, A.I.[Andreea-Iuliana], Savencu, O.[Olivian], Ristea, N.C.[Nicolae-Catalin], Verga, N.[Nicolae], Khan, F.S.[Fahad Shahbaz],
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution,
WACV23(2194-2204)
IEEE DOI 2302
Convolutional codes, Head, Magnetic resonance imaging, Computed tomography, Superresolution, Magnetic heads, Data models, Biomedical/healthcare/medicine BibRef

Qin, Y.G.[Ye-Guang], Tuerxun, P.[Palidan], Tang, F.X.[Feng-Xiao], Qian, Y.R.[Yu-Rong], Zhao, M.[Ming], Zhu, Y.[Yusen],
Feature Fusion Super Resolution Network with Gradient Guidance,
ICPR22(147-153)
IEEE DOI 2212
Visualization, PSNR, Image edge detection, Computational modeling, Superresolution, Feature extraction, Image restoration BibRef

Hong, C.[Cheeun], Baik, S.[Sungyong], Kim, H.[Heewon], Nah, S.[Seungjun], Lee, K.M.[Kyoung Mu],
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution,
ECCV22(VII:367-383).
Springer DOI 2211
BibRef

Chen, B.[Bohong], Lin, M.[Mingbao], Sheng, K.[Kekai], Zhang, M.[Mengdan], Chen, P.X.[Pei-Xian], Li, K.[Ke], Cao, L.J.[Liu-Juan], Ji, R.R.[Rong-Rong],
ARM: Any-Time Super-Resolution Method,
ECCV22(XIX:254-270).
Springer DOI 2211
BibRef

Li, Y.[Youwei], Huang, H.B.[Hai-Bin], Jia, L.[Lanpeng], Fan, H.Q.[Hao-Qiang], Liu, S.C.[Shuai-Cheng],
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution,
ECCV22(XIX:379-394).
Springer DOI 2211
BibRef

Laroche, C.[Charles], Tassano, M.[Matias],
Bridging the Domain Gap in Real World Super-Resolution,
ICIP22(2476-2480)
IEEE DOI 2211
Degradation, Training, Computational modeling, Superresolution, Pipelines, Neural networks, Training data, Super-resolution, CNN, Real-world data BibRef

Blattmann, A.[Andreas], Rombach, R.[Robin], Ling, H.[Huan], Dockhorn, T.[Tim], Kim, S.W.[Seung Wook], Fidler, S.[Sanja], Kreis, K.[Karsten],
Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models,
CVPR23(22563-22575)
IEEE DOI 2309
BibRef

Rombach, R.[Robin], Blattmann, A.[Andreas], Lorenz, D.[Dominik], Esser, P.[Patrick], Ommer, B.[Björn],
High-Resolution Image Synthesis with Latent Diffusion Models,
CVPR22(10674-10685)
IEEE DOI 2210
Training, Visualization, Image synthesis, Computational modeling, Noise reduction, Superresolution, Process control, Image and video synthesis and generation BibRef

Kong, X.T.[Xiang-Tao], Liu, X.[Xina], Gu, J.J.[Jin-Jin], Qiao, Y.[Yu], Dong, C.[Chao],
Reflash Dropout in Image Super-Resolution,
CVPR22(5992-6002)
IEEE DOI 2210
Superresolution, Pattern recognition, Task analysis, Low-level vision BibRef

Huang, Y.X.[Yi-Xuan], Zhang, X.Y.[Xiao-Yun], Fu, Y.[Yu], Chen, S.[Siheng], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], He, D.[Dazhi],
Task Decoupled Framework for Reference-based Super-Resolution,
CVPR22(5921-5930)
IEEE DOI 2210
Design methodology, Superresolution, Interference, Benchmark testing, Feature extraction, Pattern recognition, Low-level vision BibRef

Yoon, Y.[Youngho], Chung, I.[Inchul], Wang, L.[Lin], Yoon, K.J.[Kuk-Jin],
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation,
CVPR22(5667-5676)
IEEE DOI 2210
Surface reconstruction, Superresolution, Image representation, Benchmark testing, Network architecture, Feature extraction, Image and video synthesis and generation BibRef

Lee, J.W.[Jae-Won], Jin, K.H.[Kyong Hwan],
Local Texture Estimator for Implicit Representation Function,
CVPR22(1919-1928)
IEEE DOI 2210
Visualization, Image texture, Superresolution, Computer architecture, Feature extraction, Frequency estimation, Explainable computer vision BibRef

Song, K.U.[Ki-Ung], Shim, D.[Dongseok], Kim, K.W.[Kang-Wook], Lee, J.Y.[Jae-Young], Kim, Y.G.[Young-Geun],
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow,
NTIRE22(967-976)
IEEE DOI 2210
Degradation, Training, Image quality, Visualization, Superresolution, Prediction algorithms, Information filters BibRef

Li, J.J.[Jiao-Jiao], Du, S.C.[Song-Cheng], Wu, C.X.[Chao-Xiong], Leng, Y.H.[Yi-Hong], Song, R.[Rui], Li, Y.S.[Yun-Song],
DRCR Net: Dense Residual Channel Re-calibration Network with Non-local Purification for Spectral Super Resolution,
NTIRE22(1258-1267)
IEEE DOI 2210
Image resolution, Image coding, Purification, Customer relationship management, Lighting, Interference BibRef

Wang, L.[Li], Li, D.[Dong], Tian, L.[Lu], Shan, Y.[Yi],
Efficient Image Super-Resolution with Collapsible Linear Blocks,
NTIRE22(816-822)
IEEE DOI 2210
Training, Costs, Network topology, Convolution, Superresolution, Computer architecture BibRef

Li, Z.Y.[Zhe-Yuan], Liu, Y.Q.[Ying-Qi], Chen, X.Y.[Xiang-Yu], Cai, H.M.[Hao-Ming], Gu, J.J.[Jin-Jin], Qiao, Y.[Yu], Dong, C.[Chao],
Blueprint Separable Residual Network for Efficient Image Super-Resolution,
NTIRE22(832-842)
IEEE DOI 2210
Performance evaluation, Convolution, Computational modeling, Superresolution, Redundancy, Complexity theory, Pattern recognition BibRef

Du, Z.[Zongcai], Liu, D.[Ding], Liu, J.[Jie], Tang, J.[Jie], Wu, G.S.[Gang-Shan], Fu, L.[Lean],
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution,
NTIRE22(852-861)
IEEE DOI 2210
Runtime, Codes, Convolution, Memory management, Superresolution BibRef

Zhang, W.L.[Wen-Long], Shi, G.Y.[Guang-Yuan], Liu, Y.H.[Yi-Hao], Dong, C.[Chao], Wu, X.M.[Xiao-Ming],
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds,
NTIRE22(526-535)
IEEE DOI 2210
Degradation, Analytical models, Upper bound, Superresolution, Logic gates BibRef

Magid, S.A.[Salma Abdel], Lin, Z.[Zudi], Wei, D.L.[Dong-Lai], Zhang, Y.[Yulun], Gu, J.J.[Jin-Jin], Pfister, H.[Hanspeter],
Texture-based Error Analysis for Image Super-Resolution,
CVPR22(2108-2117)
IEEE DOI 2210
Measurement, Error analysis, Computational modeling, Biological system modeling, Superresolution, Semantics, Vision applications and systems BibRef

Magid, S.A.[Salma Abdel], Zhang, Y.L.[Yu-Lun], Wei, D.L.[Dong-Lai], Jang, W.D.[Won-Dong], Lin, Z.[Zudi], Fu, Y.[Yun], Pfister, H.[Hanspeter],
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution,
ICCV21(4268-4277)
IEEE DOI 2203
Visualization, Image texture, Filtering, Frequency-domain analysis, Superresolution, Convolutional neural networks, Vision applications and systems BibRef

Fuoli, D.[Dario], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Fourier Space Losses for Efficient Perceptual Image Super-Resolution,
ICCV21(2340-2349)
IEEE DOI 2203
Training, Image quality, Time-frequency analysis, Runtime, Superresolution, Generators, Image restoration, Image and video synthesis BibRef

Wang, T.F.[Teng-Fei], Xie, J.X.[Jia-Xin], Sun, W.X.[Wen-Xiu], Yan, Q.[Qiong], Chen, Q.F.[Qi-Feng],
Dual-Camera Super-Resolution with Aligned Attention Modules,
ICCV21(1981-1990)
IEEE DOI 2203
Training, Bridges, Visualization, Codes, Computational modeling, Superresolution, Computational photography, Image and video synthesis BibRef

Korkmaz, C.[Cansu], Tekalp, A.M.[A.Murat], Dogan, Z.[Zafer],
Two-Stage Domain Adapted Training for Better Generalization In Real-World Image Restoration and Super-Resolution,
ICIP21(569-573)
IEEE DOI 2201
Training, Degradation, Adaptation models, Inverse problems, Superresolution, Image restoration, image super-resolution, overfitting degradation model BibRef

Rad, M.S.[Mohammad Saeed], Yu, T.[Thomas], Bozorgtabar, B.[Behzad], Thiran, J.P.[Jean-Philippe],
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images,
AIM21(1845-1854)
IEEE DOI 2112
Training, Radio frequency, Correlation, Image color analysis, Superresolution BibRef

Jo, Y.[Younghyun], Oh, S.W.[Seoung Wug], Vajda, P.[Peter], Kim, S.J.[Seon Joo],
Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation,
CVPR21(16231-16240)
IEEE DOI 2111
Training, Adaptive systems, Impedance matching, Superresolution, Training data, Network architecture BibRef

Kim, S.Y.[Soo Ye], Sim, H.[Hyeonjun], Kim, M.C.[Mun-Churl],
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment,
CVPR21(10606-10615)
IEEE DOI 2111
Degradation, Codes, Filtering, Superresolution, Cameras, Data models BibRef

Wang, L.G.[Long-Guang], Wang, Y.Q.[Ying-Qian], Lu, L.Y.[Li-Ying], Li, W.B.[Wen-Bo], Tao, X.[Xin], Lu, J.B.[Jiang-Bo], Jia, J.Y.[Jia-Ya],
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution,
CVPR21(6364-6373)
IEEE DOI 2111
Adaptation models, Computational modeling, Superresolution, Feature extraction, Robustness, Computational efficiency BibRef

Huang, Z.W.[Zhe-Wei], Huang, A.[Ailin], Hu, X.T.[Xiao-Tao], Hu, C.[Chen], Xu, J.[Jun], Zhou, S.C.[Shu-Chang],
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution,
WACV24(4216-4227)
IEEE DOI 2404
Training, Visualization, Superresolution, Pipelines, Estimation, Training data, Streaming media, Algorithms, image and video synthesis BibRef

Xu, G.[Gang], Xu, J.[Jun], Li, Z.[Zhen], Wang, L.[Liang], Sun, X.[Xing], Cheng, M.M.[Ming-Ming],
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution,
CVPR21(6384-6393)
IEEE DOI 2111
Training, Interpolation, Convolution, Superresolution, Modulation, Bidirectional control, Benchmark testing BibRef

Gutierrez, N.B.[Nolan B.], Beksi, W.J.[William J.],
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields,
CVS21(3-13).
Springer DOI 2109
BibRef

Helminger, L.[Leonhard], Bernasconi, M.[Michael], Djelouah, A.[Abdelaziz], Gross, M.[Markus], Schroers, C.[Christopher],
Generic Image Restoration with Flow Based Priors,
NTIRE21(334-343)
IEEE DOI 2109
Degradation, Training, Noise reduction, Neural networks, Superresolution, Image restoration, Complexity theory BibRef

Lazzaretti, M.[Marta], Rebegoldi, S.[Simone], Calatroni, L.[Luca], Estatico, C.[Claudio],
A Scaled and Adaptive Fista Algorithm for Signal-dependent Sparse Image Super-resolution Problems,
SSVM21(242-253).
Springer DOI 2106
BibRef

Pragliola, M.[Monica], Calatroni, L.[Luca], Lanza, A.[Alessandro], Sgallari, F.[Fiorella],
Residual Whiteness Principle for Automatic Parameter Selection in l2-l2 Image Super-Resolution Problems,
SSVM21(476-488).
Springer DOI 2106
BibRef

Li, L.X.[Long-Xi], Feng, H.[Hesen], Zheng, B.[Bing], Ma, L.H.[Li-Hong], Tian, J.[Jing],
DID: A Nested Dense in Dense Structure with Variable Local Dense Blocks for Super-Resolution Image Reconstruction,
ICPR21(2582-2589)
IEEE DOI 2105
Multiplexing, Chaos, Visualization, Dictionaries, Superresolution, Feature extraction, Explosions, High X SR, RDN, Dense in Dense (DID), Feature aggregation BibRef

Zhou, L.G.[Li-Guo], Chen, G.[Guang], Feng, M.Y.[Ming-Yue], Knoll, A.[Alois],
Improving Low-Resolution Image Classification by Super-Resolution with Enhancing High-Frequency Content,
ICPR21(1972-1978)
IEEE DOI 2105
Training, Degradation, Visualization, Superresolution, Noise reduction, High frequency BibRef

Li, Q.[Qiang], Dai, T.[Tao], Xia, S.T.[Shu-Tao],
Progressive Splitting and Upscaling Structure for Super-Resolution,
ICPR21(8885-8891)
IEEE DOI 2105
Image quality, Convolution, Aggregates, Superresolution, Network architecture, Feature extraction, Computational efficiency BibRef

Liang, J.Y.[Jing-Yun], Sun, G.[Guolei], Zhang, K.[Kai], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution,
ICCV21(4076-4085)
IEEE DOI 2203
Degradation, Training, Visualization, Convolution, Computational modeling, Superresolution, Estimation, BibRef

Lugmayr, A.[Andreas], Danelljan, M.[Martin], Yu, F.[Fisher], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution,
WACV22(874-883)
IEEE DOI 2202
Visualization, Codes, Superresolution, Context modeling, Computational Photography, Image and Video Synthesis BibRef

Bühler, M.C.[Marcel C.], Romero, A.[Andrés], Timofte, R.[Radu],
Deepsee: Deep Disentangled Semantic Explorative Extreme Super-resolution,
ACCV20(IV:624-642).
Springer DOI 2103
BibRef

Kim, S.J.[Si-Jin], Ahn, N.[Namhyuk], Sohn, K.A.[Kyung-Ah],
Restoring Spatially-heterogeneous Distortions Using Mixture of Experts Network,
ACCV20(II:185-201).
Springer DOI 2103
BibRef

El Helou, M.[Majed], Zhou, R.F.[Ruo-Fan], Süsstrunk, S.[Sabine],
Stochastic Frequency Masking to Improve Super-resolution and Denoising Networks,
ECCV20(XVI: 749-766).
Springer DOI 2010
BibRef

Li, H.X.[Hui-Xia], Yan, C.Q.[Chen-Qian], Lin, S.H.[Shao-Hui], Zheng, X.W.[Xia-Wu], Zhang, B.C.[Bao-Chang], Yang, F.[Fan], Ji, R.R.[Rong-Rong],
PAMS: Quantized Super-resolution via Parameterized Max Scale,
ECCV20(XXV:564-580).
Springer DOI 2011
BibRef

Park, J.Y., Choi, D.Y., Song, B.C.,
Slice-Based Super-Resolution Using Light-Weight Network With Relation Loss,
ICIP20(503-507)
IEEE DOI 2011
Convolution, Image resolution, Visualization, Radio frequency, Computer architecture, Degradation, Training, Light-weight, relation loss BibRef

Hyun, S.[Sangeek], Heo, J.P.[Jae-Pil],
Varsr: Variational Super-resolution Network for Very Low Resolution Images,
ECCV20(XXIII:431-447).
Springer DOI 2011
BibRef

Jiang, Y., Lu, Y., Dong, L., Xu, W.,
Multi-frame Image Super-Resolution Algorithm Based on Small Amount of Data,
ICIVC20(118-122)
IEEE DOI 2009
Image reconstruction, Spatial resolution, Kernel, Interpolation, Estimation, Filtering, multi-frame, super-resolution, image details BibRef

Shim, G., Park, J., Kweon, I.S.,
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution,
CVPR20(8422-8431)
IEEE DOI 2008
Feature extraction, Convolution, Image reconstruction, Image resolution, Degradation, Robustness, Kernel BibRef

Shang, T., Dai, Q., Zhu, S., Yang, T., Guo, Y.,
Perceptual Extreme Super Resolution Network with Receptive Field Block,
NTIRE20(1778-1787)
IEEE DOI 2008
Image resolution, Feature extraction, Convolution, Kernel, Time complexity, Interpolation, Image reconstruction BibRef

Shoeiby, M., Petersson, L., Armin, M.A., Aliakbarian, S., Robles-Kelly, A.,
Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network,
WACV20(2793-2802)
IEEE DOI 2006
Spatial resolution, Sensors, Cameras, Image sensors, Image color analysis BibRef

Lutio, R.D., d'Aronco, S., Wegner, J.D., Schindler, K.,
Guided Super-Resolution As Pixel-to-Pixel Transformation,
ICCV19(8828-8836)
IEEE DOI 2004
image resolution, multilayer perceptrons, guided super-resolution, pixel-to-pixel transformation, Vegetation BibRef

Gu, J.J.[Jin-Jin], Lu, H.N.[Han-Nan], Zuo, W.M.[Wang-Meng], Dong, C.[Chao],
Blind Super-Resolution With Iterative Kernel Correction,
CVPR19(1604-1613).
IEEE DOI 2002
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Xu, R.[Ruikang], Yao, M.[Mingde], Xiong, Z.W.[Zhi-Wei],
Zero-Shot Dual-Lens Super-Resolution,
CVPR23(9130-9139)
IEEE DOI 2309
BibRef

Chen, C.[Chang], Xiong, Z.W.[Zhi-Wei], Tian, X.M.[Xin-Mei], Zha, Z.J.[Zheng-Jun], Wu, F.[Feng],
Camera Lens Super-Resolution,
CVPR19(1652-1660).
IEEE DOI 2002
BibRef

Xiao, J., Zhao, R., Lai, S., Jia, W., Lam, K.,
Deep Progressive Convolutional Neural Network for Blind Super-Resolution With Multiple Degradations,
ICIP19(2856-2860)
IEEE DOI 1910
blind super-resolution, deep progressive network BibRef

Bian, J.Y.[Jun-Yi], Lin, B.[Baojun], Zhang, K.[Ke],
Hybrid Function Sparse Representation Towards Image Super Resolution,
CAIP19(II:27-37).
Springer DOI 1909
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Dai, D.X.[Deng-Xin], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Jointly Optimized Regressors for Image Super-resolution,
EuroGraphics15(xx-yy).
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Michelini, P.N.[Pablo Navarrete], Zhu, D.[Dan], Liu, H.[Hanwen],
Multi-scale Recursive and Perception-Distortion Controllable Image Super-Resolution,
PerceptualRest18(V:3-19).
Springer DOI 1905
BibRef

Unni, V.S., Chaudhury, K.N.,
Non-Local Patch-Based Regularization for Image Restoration,
ICIP18(1108-1112)
IEEE DOI 1809
TV, Computational modeling, Noise reduction, Image restoration, Optimization, Image resolution, Standards, regularization, patch, ADMM, super-resolution BibRef

Chang, C.Y.[Chia-Yang], Tu, W.C.[Wei-Chih], Chien, S.Y.[Shao-Yi],
Optimized Regressor Forest for Image Super-Resolution,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Cuellar-Fierro, J.F.[Jhon F.], Vargas-Cardona, H.D.[Hernán Darío], Álvarez, A.M.[Andrés M.], Orozco, Á.A.[Álvaro A.], Álvarez, M.A.[Mauricio A.],
Non-stationary Generalized Wishart Processes for Enhancing Resolution over Diffusion Tensor Fields,
ISVC18(371-381).
Springer DOI 1811
BibRef
Earlier: A1, A2, A5, A3, A4:
Non-stationary Multi-output Gaussian Processes for Enhancing Resolution over Diffusion Tensor Fields,
CIARP17(168-176).
Springer DOI 1802
BibRef

Vargas-Cardona, H.D.[Hernán Darío], Álvarez, M.A.[Mauricio A.], Orozco, Á.A.[Álvaro A.],
Generalized Wishart Processes for Interpolation Over Diffusion Tensor Fields,
ISVC15(II: 499-508).
Springer DOI 1601
BibRef

Gao, Q.Q.[Qin-Quan], Zhao, Y.[Yan], Li, G.[Gen], Tong, T.[Tong],
Image Super-Resolution Using Knowledge Distillation,
ACCV18(II:527-541).
Springer DOI 1906
BibRef

Klatzer, T.[Teresa], Soukup, D.[Daniel], Kobler, E.[Erich], Hammernik, K.[Kerstin], Pock, T.[Thomas],
Trainable Regularization for Multi-frame Superresolution,
GCPR17(90-100).
Springer DOI 1711
BibRef

Malczewski, K.[Krzysztof],
Motion artifacts free image resolution enhancement exploiting image priors,
WSSIP17(1-4)
IEEE DOI 1707
Algorithm design and analysis, Estimation, Kernel, Signal resolution, Spatial resolution, image enhancement, super-resolution BibRef

Bodduna, K.[Kireeti], Weickert, J.[Joachim], Cárdenas, M.[Marcelo],
Multi-frame Super-resolution from Noisy Data,
SSVM21(565-577).
Springer DOI 2106
BibRef

Bodduna, K.[Kireeti], Weickert, J.[Joachim],
Evaluating Data Terms for Variational Multi-frame Super-Resolution,
SSVM17(590-601).
Springer DOI 1706
BibRef

Chen, X., Zhai, G., Wang, J., Hu, C., Chen, Y.,
Color guided thermal image super resolution,
VCIP16(1-4)
IEEE DOI 1701
Cameras BibRef

Gupta, A., Johnson, J.[Justin], Alahi, A.[Alexandre], Fei-Fei, L.[Li],
Characterizing and Improving Stability in Neural Style Transfer,
ICCV17(4087-4096)
IEEE DOI 1802
matrix algebra, recurrent neural nets, video signal processing, Gram matrix representing style, neural style transfer, Videos BibRef

Johnson, J.[Justin], Alahi, A.[Alexandre], Fei-Fei, L.[Li],
Perceptual Losses for Real-Time Style Transfer and Super-Resolution,
ECCV16(II: 694-711).
Springer DOI 1611
BibRef

Tao, Y., Muller, J.P.,
Quantitative Assessment Of A Novel Super-resolution Restoration Technique Using Hirise With Navcam Images: How Much Resolution Enhancement Is Possible From Repeat-pass Observations,
ISPRS16(B4: 503-509).
DOI Link 1610
BibRef

Qu, C., Luo, D., Monari, E., Schuchert, T., Beyerer, J.,
Capturing ground truth super-resolution data,
ICIP16(2812-2816)
IEEE DOI 1610
Cameras BibRef

Bätz, M., Eichenseer, A., Kaup, A.,
Multi-image super-resolution using a dual weighting scheme based on Voronoi tessellation,
ICIP16(2822-2826)
IEEE DOI 1610
Image restoration BibRef

Dai, D., Wang, Y., Chen, Y., Van Gool, L.J.,
Is image super-resolution helpful for other vision tasks?,
WACV16(1-9)
IEEE DOI 1606
Image resolution BibRef

Bai, Y., Jia, H., Xie, X., Chen, R., Jiang, M., Gao, W.,
A fast super-resolution method based on sparsity properties,
VCIP15(1-4)
IEEE DOI 1605
Estimation BibRef

Hirao, D.[Daiki], Iyatomi, H.[Hitoshi],
Prototype of Super-Resolution Camera Array System,
ISVC15(I: 911-920).
Springer DOI 1601
BibRef

Markopoulos, P.P.[Panos P.], Kundu, S.[Sandipan], Pados, D.A.[Dimitris A.],
L1-fusion: Robust linear-time image recovery from few severely corrupted copies,
ICIP15(1225-1229)
IEEE DOI 1512
Image recovery BibRef

Tang, H.X.[Hui-Xuan], Zhang, X.P.[Xiao-Peng], Zhuo, S.J.[Shao-Jie], Chen, F.[Feng], Kutulakos, K.N., Shen, L.[Liang],
High Resolution Photography with an RGB-Infrared Camera,
ICCP15(1-10)
IEEE DOI 1511
colour photography BibRef

Prasoon, A.[Adhish], Chaubey, H.[Himanshu], Gupta, A.[Abhinav], Garg, R.[Rohit], Chaudhury, S.[Santanu],
A Novel Approach for Image Super Resolution Using Kernel Methods,
PReMI15(126-135).
Springer DOI 1511
BibRef

Lee, H.S.[Hyun-Seung],
A new image super resolution by texture transfer,
ICIP14(3915-3918)
IEEE DOI 1502
Discrete cosine transforms BibRef

Xu, J.[Jian], Qi, C.[Chun], Chang, Z.G.[Zhi-Guo],
Coupled K-SVD dictionary training for super-resolution,
ICIP14(3910-3914)
IEEE DOI 1502
Approximation algorithms BibRef

Lin, S.[Sina], Qin, Z.C.[Zeng-Chang], Liao, R.J.[Ren-Jie], Wan, T.[Tao],
A confidence growing model for super-resolution,
ICIP14(3929-3933)
IEEE DOI 1502
Dictionaries BibRef

Hu, W.[Wei], Cheung, G.[Gene], Li, X.[Xin], Au, O.C.[Oscar C.],
Graph-based joint denoising and super-resolution of generalized piecewise smooth images,
ICIP14(2056-2060)
IEEE DOI 1502
Image resolution BibRef

Cho, C.H.[Chang-Hun], Jeon, J.H.[Jae-Hwan], Paik, J.[Joonki],
Example-based super-resolution using self-patches and approximated constrained least squares filter,
ICIP14(2140-2144)
IEEE DOI 1502
Degradation BibRef

Hidane, M., Aujol, J.F., Berthoumieu, Y., Deledalle, C.A.,
Super-resolution from a low- and partial high-resolution image pair,
ICIP14(2145-2149)
IEEE DOI 1502
Image reconstruction BibRef

Singh, A.[Abhishek], Ahuja, N.[Narendra],
Super-Resolution Using Sub-Band Self-Similarity,
ACCV14(II: 552-568).
Springer DOI 1504
BibRef
And:
Sub-band Energy Constraints for Self-Similarity Based Super-resolution,
ICPR14(4447-4452)
IEEE DOI 1412
Attenuation BibRef

Goto, T.[Tomio], Fukuoka, T.[Takafumi], Nagashima, F.[Fumiya], Hirano, S.[Satoshi], Sakurai, M.[Masaru],
Super-resolution System for 4K-HDTV,
ICPR14(4453-4458)
IEEE DOI 1412
Electric shock BibRef

Hsiao, W.T.[Wei-Tsung], Leou, J.J.[Jing-Jang], Hsiao, H.H.[Han-Hui],
Super-resolution Reconstruction for Binocular 3D Data,
ICPR14(4206-4211)
IEEE DOI 1412
Computational modeling BibRef

Oberdorster, A., Favaro, P., Lensch, H.P.A.,
Anamorphic pixels for multi-channel superresolution,
ICCP14(1-10)
IEEE DOI 1411
image reconstruction BibRef

Shi, B.X.[Bo-Xin], Zhao, H.[Hang], Ben-Ezra, M.[Moshe], Yeung, S.K.[Sai-Kit], Fernandez-Cull, C.[Christy], Shepard, R.H.[R. Hamilton], Barsi, C.[Christopher], Raskar, R.[Ramesh],
Sub-pixel Layout for Super-Resolution with Images in the Octic Group,
ECCV14(I: 250-264).
Springer DOI 1408
BibRef

Wang, Z.Y.[Zhang-Yang], Yang, Y., Yang, J.C.[Jian-Chao], Huang, T.S.[Thomas S.],
Designing a composite dictionary adaptively from joint examples,
VCIP15(1-4)
IEEE DOI 1605
Adaptation models BibRef

Wang, Z.Y.[Zhang-Yang], Wang, Z.W.[Zhao-Wen], Chang, S.Y.[Shi-Yu], Yang, J.C.[Jian-Chao], Huang, T.S.[Thomas S.],
A joint perspective towards image super-resolution: Unifying external- and self-examples,
WACV14(596-603)
IEEE DOI 1406
BibRef

Bahat, Y., Michaeli, T.[Tomer],
Explorable Super Resolution,
CVPR20(2713-2722)
IEEE DOI 2008
Image resolution, Image reconstruction, Heart rate, Graphical user interfaces, Neural networks, Impedance matching BibRef

Michaeli, T.[Tomer], Irani, M.[Michal],
Nonparametric Blind Super-resolution,
ICCV13(945-952)
IEEE DOI 1403
BibRef

Ho-Phuoc, T.[Tien], Dupret, A.[Antoine], Alacoque, L.[Laurent],
Super resolution method adapted to spatial contrast,
ICIP13(976-980)
IEEE DOI 1402
Image reconstruction BibRef

Sakurai, M.[Masaru], Sakuta, Y.[Yasuhiro], Watanabe, M.[Masashi], Goto, T.[Tomio], Hirano, S.[Satoshi],
Super-resolution through non-linear enhancement filters,
ICIP13(854-858)
IEEE DOI 1402
Electric shock BibRef

Ono, S.[Shunsuke], Yamada, I.[Isao],
Optimized JPEG image decompression with super-resolution interpolation using multi-order total variation,
ICIP13(474-478)
IEEE DOI 1402
Discrete cosine transforms BibRef

Gao, J.B.[Jun-Bin], Guo, Y.[Yi], Yin, M.[Ming],
Restricted Boltzmann machine approach to couple dictionary training for image super-resolution,
ICIP13(499-503)
IEEE DOI 1402
Dictionaries BibRef

Sun, L.B.[Li-Bin], Hays, J.,
Super-resolution from internet-scale scene matching,
ICCP12(1-12).
IEEE DOI 1208
BibRef

Park, Y.J.[Young-Jin], Yoo, S.I.[Suk I.],
Isotropic Huber MRFS for structure super-resolution,
ICIP11(1137-1140).
IEEE DOI 1201
BibRef

Sroubek, F.[Filip], Kamenicky, J.[Jan], Milanfar, P.[Peyman],
Superfast superresolution,
ICIP11(1153-1156).
IEEE DOI 1201
BibRef

Zhao, Y.[Ying], Shen, J.B.[Jian-Bing], He, Y.[Ying],
Subband Architecture Based Exposure Fusion,
PSIVT10(501-506).
IEEE DOI 1011
BibRef

Ploquin, M.[Marie], Kouame, D.[Denis],
Improvement of medical image resolution using an extended 2D factorized form complex number parametric model,
ICIP10(601-604).
IEEE DOI 1009
BibRef

Harmeling, S.[Stefan], Sra, S.[Suvrit], Hirsch, M.[Michael], Scholkopf, B.[Bernhard],
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM,
ICIP10(3313-3316).
IEEE DOI 1009

See also Fast removal of non-uniform camera shake. BibRef

Hirsch, M.[Michael], Sra, S.[Suvrit], Scholkopf, B.[Bernhard], Harmeling, S.[Stefan],
Efficient filter flow for space-variant multiframe blind deconvolution,
CVPR10(607-614).
IEEE DOI 1006
BibRef
Earlier: A4, A1, A2, A3:
Online blind deconvolution for astronomical imaging,
ICCP09(1-7).
IEEE DOI 1208
BibRef

Ozcelikkale, A.[Ayca], Akar, G.B.[Gozde B.], Ozaktas, H.M.[Haldun M.],
Super-resolution using multiple quantized images,
ICIP10(2029-2032).
IEEE DOI 1009
BibRef

Sun, J.[Jian], Zhu, J.J.[Jie-Jie], Tappen, M.F.[Marshall F.],
Context-constrained hallucination for image super-resolution,
CVPR10(231-238).
IEEE DOI 1006
Given initial set of high-low resolution image segments. BibRef

Shi, G.M.[Guang-Ming], Gao, D.H.[Da-Hua], Liu, D.H.[Dan-Hua], Wang, L.J.[Liang-Jun],
High resoluton image reconstruction: A new imager via movable random exposure,
ICIP09(1177-1180).
IEEE DOI 0911
Randomly fluttering shutter, moving camera. BibRef

Turgay, E.[Emre], Akar, G.B.[Gozde B.],
Directionally adaptive super-resolution,
ICIP09(1201-1204).
IEEE DOI 0911
BibRef

Buades, T., Lou, Y., Morel, J.M., Tang, Z.W.[Zhong-Wei],
A note on multi-image denoising,
LNLA09(1-15).
IEEE DOI 0908
BibRef

Ebrahimi, M., Vrscay, E.R., Martel, A.L.,
Coupled multi-frame super-resolution with diffusive motion model and total variation regularization,
LNLA09(62-69).
IEEE DOI 0908
BibRef

Mudugamuwa, D.J.[Damith J.], He, X.J.[Xiang-Jian], Ahn, C.H.[Chung-Hyun], Yang, J.[Jie],
Higher order prediction for sub-pixel motion estimation,
ICIP09(1585-1588).
IEEE DOI 0911
BibRef

Mudugamuwa, D.J., He, X.J.[Xiang-Jian], Wei, D.M.[Da-Ming], Ahn, C.H.[Chung-Hyun],
Super-resolution by prediction based sub-pel motion estimation,
IVCNZ09(282-287).
IEEE DOI 0911
BibRef

Mudugamuwa, D.J.[Damith J.], Jia, W.J.[Wen-Jing], He, X.J.[Xiang-Jian],
Asymmetric, Non-unimodal Kernel Regression for Image Processing,
DICTA10(141-145).
IEEE DOI 1012
BibRef

Zhan, Q.F.[Qiu-Fang], Gao, X.M.[Xiu-Min], Li, J.S.[Jin-Song], Zhuang, S.L.[Song-Lin],
Resolution Enhancement in High Numerical Aperture Optical System,
CISP09(1-5).
IEEE DOI 0910
BibRef

Ma, Y.J.[Yan-Jie], Zhang, H.[Hua], Xue, Y.B.[Yan-Bing],
A Novel Super-Resolution Image Reconstruction Based on MRF,
CISP09(1-4).
IEEE DOI 0910
BibRef

Jin, Z.[Zhang], Zhong, W.[Wang], Hui, Z.G.[Zhou Guang], Hua, Y.S.[Ye Sheng],
Research of Super-Resolution Reconstruction Based on Multi-Images of Random Micro-Offset,
CISP09(1-5).
IEEE DOI 0910
BibRef

Hou, P.[Peng], Xu, W.H.[Wen-Hai],
Super Resolution Time Delay Estimation for Underwater Acoustic Sinusoidal Signals,
CISP09(1-6).
IEEE DOI 0910
BibRef

Ebrahimi, M.[Mehran], Martel, A.L.[Anne L.],
A PDE Approach to Coupled Super-Resolution with Non-parametric Motion,
EMMCVPR09(112-125).
Springer DOI 0908
BibRef

Toronto, N.[Neil], Morse, B.S.[Bryan S.], Seppi, K.[Kevin], Ventura, D.[Dan],
Super-resolution via recapture and Bayesian effect modeling,
CVPR09(2388-2395).
IEEE DOI 0906
BibRef

Liu, Y.[Ying], Fieguth, P.W.[Paul W.],
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction,
EMMCVPR09(70-83).
Springer DOI 0908
BibRef
And:
Image Resolution Enhancement with Hierarchical Hidden Fields,
ICIAR09(73-82).
Springer DOI 0907
BibRef

Ni, K.S.[Karl S.], Nguyen, T.Q.[Truong Q.],
Color Image Superresolution Based on a Stochastic Combinational Classification-Regression Algorithm,
ICIP07(II: 89-92).
IEEE DOI 0709
BibRef

Qiao, J.P.[Jian-Ping], Liu, J.[Ju],
HOS-Based Image Super-Resolution Reconstruction,
MCAM07(213-222).
Springer DOI 0706
BibRef

Chatterjee, P.[Priyam], Namboodiri, V.P.[Vinay P.], Chaudhuri, S.[Subhasis],
Super-Resolution Using Sub-band Constrained Total Variation,
SSVM07(616-627).
Springer DOI 0705
BibRef

Damera-Venkata, N.[Niranjan], hang, N.L.C.[Nelson L.C],
On the Resolution Limits of Superimposed Projection,
ICIP07(V: 373-376).
IEEE DOI 0709
BibRef
And:
Realizing Super-Resolution with Superimposed Projection,
PROCAMS07(1-8).
IEEE DOI 0706
BibRef

Lv, J.Y.[Jie-Yong], Hao, P.W.[Peng-Wei],
In-Focus Imaging by Mosaicking and Super-Resolution,
ICIP06(2689-2692).
IEEE DOI 0610
BibRef

van Eekeren, A., Schutte, K., Dijk, J., de Lange, D.J.J., van Vliet, L.J.,
Super-Resolution on Moving Objects and Background,
ICIP06(2709-2712).
IEEE DOI 0610
BibRef

Lian, H.[Heng],
Variational Local Structure Estimation for Image Super-Resolution,
ICIP06(1721-1724).
IEEE DOI 0610
BibRef

El-Hakim, S.,
A sequential approach to capture fine geometric details from images,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Sasahara, R., Hasegawa, H., Yamada, I., Sakaniwa, K.,
A Color Super-Resolution with Multiple Nonsmooth Constraints by Hybrid Steepest Descent Method,
ICIP05(I: 857-860).
IEEE DOI 0512
BibRef

Chen, M.[Mei],
Dynamic Content Adaptive Super-Resolution,
ICIAR04(I: 220-227).
Springer DOI 0409
BibRef

Yoakeim, R., Taubman, D.S.,
Quantitative analysis of resolution synthesis,
ICIP04(III: 1645-1648).
IEEE DOI 0505
BibRef

Zhao, W.Y.[Wen-Yi],
Super-resolution with significant illumination change,
ICIP04(III: 1771-1774).
IEEE DOI 0505
BibRef

Wagner, R., Nowak, R.D., Baraniuk, R.G.,
Distributed image compression for sensor networks using correspondence analysis and super-resolution,
ICIP03(I: 597-600).
IEEE DOI 0312
BibRef

Zhao, W., Sawhney, H., Hansen, M.W., Samarasekera, S.,
Super-fusion: a super-resolution method based on fusion,
ICPR02(II: 269-272).
IEEE DOI 0211
BibRef

Kämpke, T.[Thomas], Elfes, A.[Alberto], Schiekel, C.[Christian],
Estimation of Superresolution Images Using Causal Networks: The One-dimensional Case,
ICPR00(Vol I: 584-587).
IEEE DOI 0009
BibRef

Burt, P.J., and Kolczynski, R.J.,
Enhanced Image Capture Through Fusion,
ICCV93(173-182).
IEEE DOI Get better images using the multiple frames and fusing the series of images. BibRef 9300

Singh, A.,
Incremental Image Sequence Enhancement with Implicit Motion Compensation,
ICCV93(314-319).
IEEE DOI BibRef 9300

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Handheld, Burst Super Resolution .

Last update:Sep 28, 2024 at 17:47:54