Lu, Y.[Yao],
Inamura, M.[Minoru],
del Carmen Valdes, M.[Maria],
Super-resolution of the undersampled and subpixel shifted image
sequence by a neural network,
IJIST(14), No. 1, 2004, pp. 8-15.
DOI Link
0406
BibRef
Miravet, C.[Carlos],
Rodriguez, F.B.[Francisco B.],
A two-step neural-network based algorithm for fast image
super-resolution,
IVC(25), No. 9, 1 September 2007, pp. 1449-1473.
Elsevier DOI
0707
Super-resolution; Multi-layer perceptron; Probabilistic neural network;
Sequence processing; Image restoration
BibRef
Miravet, C.[Carlos],
Rodriguez, F.B.[Francisco B.],
A PCA-based super-resolution algorithm for short image sequences,
ICIP10(2025-2028).
IEEE DOI
1009
BibRef
Agrawal, D.,
Singhai, J.,
Multifocus image fusion using modified pulse coupled neural network for
improved image quality,
IET-IPR(4), No. 6, December 2010, pp. 443-451.
DOI Link
1101
BibRef
Dong, C.[Chao],
Loy, C.C.[Chen Change],
He, K.[Kaiming],
Tang, X.[Xiaoou],
Image Super-Resolution Using Deep Convolutional Networks,
PAMI(38), No. 2, February 2016, pp. 295-307.
IEEE DOI
1601
BibRef
Earlier:
Learning a Deep Convolutional Network for Image Super-Resolution,
ECCV14(IV: 184-199).
Springer DOI
1408
Convolutional codes
See also Compression Artifacts Reduction by a Deep Convolutional Network.
BibRef
Dong, C.[Chao],
Loy, C.C.[Chen Change],
Tang, X.[Xiaoou],
Accelerating the Super-Resolution Convolutional Neural Network,
ECCV16(II: 391-407).
Springer DOI
1611
BibRef
Hui, T.W.[Tak-Wai],
Loy, C.C.[Chen Change],
Tang, X.[Xiaoou],
Depth Map Super-Resolution by Deep Multi-Scale Guidance,
ECCV16(III: 353-369).
Springer DOI
1611
BibRef
Wang, L.F.[Ling-Feng],
Huang, Z.[Zehao],
Gong, Y.C.[Yong-Chao],
Pan, C.H.[Chun-Hong],
Ensemble based deep networks for image super-resolution,
PR(68), No. 1, 2017, pp. 191-198.
Elsevier DOI
1704
Super-resolution
BibRef
Huang, Z.[Zehao],
Wang, L.F.[Ling-Feng],
Meng, G.,
Pan, C.H.[Chun-Hong],
Image Super-Resolution Via Deep Dilated Convolutional Networks,
ICIP17(953-957)
IEEE DOI
1803
Acceleration, Convolution, Image reconstruction, Image resolution,
Machine learning, Task analysis, Training, Acceleration,
Super-Resolution
BibRef
Yoon, Y.,
Jeon, H.G.,
Yoo, D.,
Lee, J.Y.[Joon-Young],
Kweon, I.S.[In So],
Light-Field Image Super-Resolution Using Convolutional Neural Network,
SPLetters(24), No. 6, June 2017, pp. 848-852.
IEEE DOI
1705
BibRef
Earlier:
Learning a Deep Convolutional Network for Light-Field Image
Super-Resolution,
CVPV15(57-65)
IEEE DOI
1602
Cameras, Convolution, Neural networks, Signal resolution,
Spatial resolution, Training, Convolutional neural network,
light-field (LF) image super-resolution (SR).
Cameras; Image restoration; Lenses; Neural networks; Spatial resolution
BibRef
Shi, Y.[Yukai],
Wang, K.[Keze],
Chen, C.Y.[Chong-Yu],
Xu, L.[Li],
Lin, L.[Liang],
Structure-Preserving Image Super-Resolution via Contextualized
Multitask Learning,
MultMed(19), No. 12, December 2017, pp. 2804-2815.
IEEE DOI
1712
Context, Feature extraction, Image resolution, Image restoration,
Interpolation, Neural networks, Training, Convolutional network,
structure-preserving image super-resolution (SR)
BibRef
Mei, S.H.[Shao-Hui],
Yuan, X.[Xin],
Ji, J.Y.[Jing-Yu],
Zhang, Y.F.[Yi-Fan],
Wan, S.[Shuai],
Du, Q.[Qian],
Hyperspectral Image Spatial Super-Resolution via 3D Full
Convolutional Neural Network,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Mei, S.H.[Shao-Hui],
Yuan, X.[Xin],
Ji, J.Y.[Jing-Yu],
Wan, S.[Shuai],
Hou, J.,
Du, Q.[Qian],
Hyperspectral image super-resolution via convolutional neural network,
ICIP17(4297-4301)
IEEE DOI
1803
Distortion, Hyperspectral imaging, Image reconstruction,
Image restoration, Spatial resolution, Hyperspectral,
super-resolution
BibRef
Song, Q.,
Xiong, R.,
Liu, D.,
Xiong, Z.W.[Zhi-Wei],
Wu, F.[Feng],
Gao, W.,
Fast Image Super-Resolution via Local Adaptive Gradient Field
Sharpening Transform,
IP(27), No. 4, April 2018, pp. 1966-1980.
IEEE DOI
1802
gradient methods, image resolution, image restoration, HR image,
LR image, blurry gradient field, gradient profile structure,
upsampling
BibRef
Zhang, H.C.[Hao-Chen],
Liu, D.[Dong],
Xiong, Z.W.[Zhi-Wei],
Convolutional Neural Network-Based Video Super-Resolution for Action
Recognition,
FG18(746-750)
IEEE DOI
1806
Image recognition, Optical imaging, Optical losses,
Spatial resolution, Streaming media, Training, Action recognition,
video super-resolution
BibRef
Yu, H.C.[Hai-Chao],
Liu, D.[Ding],
Shi, H.H.[Hong-Hui],
Yu, H.C.[Han-Chao],
Wang, Z.Y.[Zhang-Yang],
Wang, X.C.[Xin-Chao],
Cross, B.[Brent],
Bramler, M.[Matthew],
Huang, T.S.[Thomas S.],
Computed Tomography Super-Resolution Using Convolutional Neural
Networks,
ICIP17(3944-3948)
IEEE DOI
1803
computerised tomography, feature extraction,
image reconstruction, image resolution,
Super-resolution (SR)
BibRef
Li, Y.[Yue],
Liu, D.[Dong],
Li, H.Q.[Hou-Qiang],
Li, L.[Li],
Li, Z.[Zhu],
Wu, F.[Feng],
Learning a Convolutional Neural Network for Image Compact-Resolution,
IP(28), No. 3, March 2019, pp. 1092-1107.
IEEE DOI
1812
data compression, image coding, image reconstruction,
image resolution, image sampling, neural nets, video coding,
up-sampling
BibRef
Fan, H.Z.[Han-Zhi],
Liu, D.[Dong],
Xiong, Z.W.[Zhi-Wei],
Wu, F.[Feng],
Two-Stage Convolutional Neural Network for Light Field
Super-Resolution,
ICIP17(1167-1171)
IEEE DOI
1803
Cameras, Convolutional neural networks, Correlation,
Spatial resolution, Training,
super-resolution
BibRef
Cheong, J.Y.,
Park, I.K.,
Deep CNN-Based Super-Resolution Using External and Internal Examples,
SPLetters(24), No. 8, August 2017, pp. 1252-1256.
IEEE DOI
1708
convolution, image resolution, neural nets,
deep CNN-based SISR method, deep CNN-based superresolution,
deep convolutional neural network,
global residual network, internal example-based SISR methods,
Deep convolutional neural network (CNN), external example,
internal example, single, image, super-resolution, (SISR)
BibRef
Huang, Y.,
Shao, L.,
Frangi, A.F.,
Cross-Modality Image Synthesis via Weakly Coupled and Geometry
Co-Regularized Joint Dictionary Learning,
MedImg(37), No. 3, March 2018, pp. 815-827.
IEEE DOI
1804
BibRef
Earlier:
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D
Medical Images Using Weakly-Supervised Joint Convolutional Sparse
Coding,
CVPR17(5787-5796)
IEEE DOI
1711
diseases, geometry, image matching, image registration,
image representation, learning (artificial intelligence),
sparse representation.
Biomedical imaging, Convolutional codes, Image coding,
Image reconstruction, Image resolution,
Training
BibRef
Zhang, F.[Fu],
Cai, N.[Nian],
Cen, G.D.[Guan-Dong],
Li, F.Y.[Fei-Yang],
Wang, H.[Han],
Chen, X.[Xindu],
Image super-resolution via a novel cascaded convolutional neural
network framework,
SP:IC(63), 2018, pp. 9-18.
Elsevier DOI
1804
Image super-resolution, Cascaded convolution neural network,
Multi-scale feature mapping, Residual learning, Gradient clipping
BibRef
Pouliot, D.[Darren],
Latifovic, R.[Rasim],
Pasher, J.[Jon],
Duffe, J.[Jason],
Landsat Super-Resolution Enhancement Using Convolution Neural
Networks and Sentinel-2 for Training,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Wang, Y.,
Liu, F.,
Zhang, K.,
Hou, G.,
Sun, Z.,
Tan, T.,
LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network
for Light-Field Image Super-Resolution,
IP(27), No. 9, September 2018, pp. 4274-4286.
IEEE DOI
1807
convolution, feedforward neural nets, image fusion,
image reconstruction, image resolution, recurrent neural nets,
super-resolution
BibRef
Ren, C.,
He, X.,
Pu, Y.,
Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super
Resolution,
SPLetters(25), No. 7, July 2018, pp. 916-920.
IEEE DOI
1807
convolution, feedforward neural nets, gradient methods,
image resolution, AHNLTV, GA-GP approach, GSR,
super resolution
BibRef
Chu, J.,
Zhang, J.,
Lu, W.,
Huang, X.,
A Novel Multiconnected Convolutional Network for Super-Resolution,
SPLetters(25), No. 7, July 2018, pp. 946-950.
IEEE DOI
1807
convolution, feedforward neural nets, image representation,
image resolution, optimisation, SISR tasks,
super-resolution
BibRef
Zhao, J.W.[Jian-Wei],
Sun, T.T.[Tian-Tian],
Cao, F.[Feilong],
Image super-resolution via adaptive sparse representation and
self-learning,
IET-CV(12), No. 5, August 2018, pp. 753-761.
DOI Link
1807
BibRef
Tan, Z.Y.[Zhen-Yu],
Yue, P.[Peng],
Di, L.P.[Li-Ping],
Tang, J.M.[Jun-Mei],
Deriving High Spatiotemporal Remote Sensing Images Using Deep
Convolutional Network,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
From High Temporal, Low Spatial resolution.
BibRef
Wen, R.[Ran],
Fu, K.[Kun],
Sun, H.[Hao],
Sun, X.[Xian],
Wang, L.[Lei],
Image Superresolution Using Densely Connected Residual Networks,
SPLetters(25), No. 10, October 2018, pp. 1565-1569.
IEEE DOI
1810
image resolution, learning (artificial intelligence),
neural nets, densely connected residual networks,
residual learning
BibRef
Lin, G.M.[Gui-Min],
Wu, Q.X.[Qing-Xiang],
Chen, L.[Liang],
Qiu, L.[Lida],
Wang, X.[Xuan],
Liu, T.J.[Tian-Jian],
Chen, X.[Xiyao],
Deep unsupervised learning for image super-resolution with generative
adversarial network,
SP:IC(68), 2018, pp. 88-100.
Elsevier DOI
1810
Super-resolution, Deep unsupervised learning,
Sub-pixel convolution, Regularizer, Generative adversarial network
BibRef
Zheng, Y.[Yan],
Cao, X.[Xiang],
Xiao, Y.[Yi],
Zhu, X.[Xianyi],
Yuan, J.[Jin],
Joint residual pyramid for joint image super-resolution,
JVCIR(58), 2019, pp. 53-62.
Elsevier DOI
1901
Deep learning, Neural convolutional pyramid,
Joint super-resolution, Residual block
BibRef
Arun, P.V.[Pattathal V.],
Herrmann, I.[Ittai],
Budhiraju, K.M.[Krishna M.],
Karnieli, A.[Arnon],
Convolutional network architectures for super-resolution/sub-pixel
mapping of drone-derived images,
PR(88), 2019, pp. 431-446.
Elsevier DOI
1901
Sub-pixel mapping, Super-resolution,
Convolutional neural network, Class distribution, Drone, UAV
BibRef
Xie, W.Y.[Wei-Ying],
Shi, Y.Z.[Yan-Zi],
Li, Y.S.[Yun-Song],
Jia, X.P.[Xiu-Ping],
Lei, J.[Jie],
High-quality spectral-spatial reconstruction using saliency detection
and deep feature enhancement,
PR(88), 2019, pp. 139-152.
Elsevier DOI
1901
Hyperspectral image, Quality enhancement, Structure tensor,
Deep neural networks, Adaptive weighting, Nonnegative matrix factorization
BibRef
Chen, Y.X.[Yan-Xiang],
Tan, H.[Huadong],
Zhang, L.[Luming],
Zhou, J.[Jie],
Lu, Q.A.[Qi-Ang],
Hybrid image super-resolution using perceptual similarity from
pre-trained network,
JVCIR(60), 2019, pp. 229-235.
Elsevier DOI
1903
Super-resolution, Hybrid method, Adaptive weight, Pre-trained VGG network
BibRef
Yang, W.,
Xia, S.,
Liu, J.,
Guo, Z.,
Reference-Guided Deep Super-Resolution via Manifold Localized
External Compensation,
CirSysVideo(29), No. 5, May 2019, pp. 1270-1283.
IEEE DOI
1905
Manifolds, Image resolution, Image reconstruction, Estimation,
Databases, Semantics, Face, Super-resolution, manifold localization,
internal structure inference
BibRef
Xu, J.,
Li, M.,
Fan, J.,
Zhao, X.,
Chang, Z.,
Self-Learning Super-Resolution Using Convolutional Principal
Component Analysis and Random Matching,
MultMed(21), No. 5, May 2019, pp. 1108-1121.
IEEE DOI
1905
convolution, feature extraction, image matching,
image reconstruction, image resolution,
principal component analysis
BibRef
Guo, D.[Dan],
Niu, Y.X.[Yan-Xiong],
Xie, P.Y.[Peng-Yan],
Speedy and accurate image super-resolution via deeply recursive CNN
with skip connection and network in network,
IET-IPR(13), No. 7, 30 May 2019, pp. 1201-1209.
DOI Link
1906
BibRef
Zhao, Z.Q.[Zhong-Qiu],
Hu, J.[Jian],
Tian, W.D.[Wei-Dong],
Ling, N.[Ning],
Cooperative Adversarial Network for Accurate Super Resolution,
ACCV18(II:98-114).
Springer DOI
1906
BibRef
Liu, J.[Jie],
Jung, C.[Cheolkon],
Multiple Connected Residual Network for Image Enhancement on
Smartphones,
PerceptualRest18(V:182-196).
Springer DOI
1905
BibRef
Hui, Z.[Zheng],
Wang, X.[Xiumei],
Deng, L.[Lirui],
Gao, X.[Xinbo],
Perception-Preserving Convolutional Networks for Image Enhancement on
Smartphones,
PerceptualRest18(V:197-213).
Springer DOI
1905
BibRef
Cheon, M.[Manri],
Kim, J.H.[Jun-Hyuk],
Choi, J.H.[Jun-Ho],
Lee, J.S.[Jong-Seok],
Generative Adversarial Network-Based Image Super-Resolution Using
Perceptual Content Losses,
PerceptualRest18(V:51-62).
Springer DOI
1905
BibRef
Kim, J.,
Lee, J.,
Deep Residual Network with Enhanced Upscaling Module for
Super-Resolution,
Restoration18(913-9138)
IEEE DOI
1812
Convolution, Image resolution, Feature extraction, Training,
Image reconstruction, Computer vision, Signal resolution
BibRef
Tan, W.,
Yan, B.,
Bare, B.,
Feature Super-Resolution: Make Machine See More Clearly,
CVPR18(3994-4002)
IEEE DOI
1812
Image resolution, Feature extraction,
Generative adversarial networks, Data models, Image recognition,
Euclidean distance
BibRef
Gao, P.,
Xue, J.,
Lu, K.,
Yan, Y.,
A fast Cascade Shape Regression Method based on CNN-based
Initialization,
ICPR18(3037-3042)
IEEE DOI
1812
Shape, Face, Training, Interpolation, Convolution,
Neural networks, Splines (mathematics)
BibRef
Jiang, T.,
Wu, X.,
Yu, Z.,
Shui, W.,
Lu, G.,
Guo, S.,
Fei, H.,
Zhang, Q.,
Recursive Inception Network for Super-Resolution,
ICPR18(2759-2764)
IEEE DOI
1812
Feature extraction, Training, Image reconstruction,
Image resolution, Periodic structures, Interpolation, Convolution
BibRef
Kasem, H.M.,
Hung, K.,
Jiang, J.,
Revised Spatial Transformer Network towards Improved Image
Super-resolutions,
ICPR18(2688-2692)
IEEE DOI
1812
Spatial resolution, Signal resolution, Image reconstruction,
Training, Transforms, Interpolation
BibRef
Gao, H.,
Chen, Z.,
Ma, G.,
Xie, W.,
Li, Z.,
Deep Pixel Probabilistic Model for Super Resolution Based on Human
Visual Saliency Mechanism,
ICPR18(2747-2752)
IEEE DOI
1812
Training, Image resolution, Probabilistic logic, Visualization,
Interpolation, Computational modeling, Optimization,
image quality assessment
BibRef
Zhang, Y.L.[Yu-Lun],
Tian, Y.,
Kong, Y.,
Zhong, B.N.[Bi-Neng],
Fu, Y.[Yun],
Residual Dense Network for Image Super-Resolution,
CVPR18(2472-2481)
IEEE DOI
1812
Feature extraction, Convolution, Image resolution, Computer vision,
Training, Buildings, Fuses
BibRef
Zhang, Y.L.[Yu-Lun],
Li, K.P.[Kun-Peng],
Li, K.[Kai],
Wang, L.C.[Li-Chen],
Zhong, B.N.[Bi-Neng],
Fu, Y.[Yun],
Image Super-Resolution Using Very Deep Residual Channel Attention
Networks,
ECCV18(VII: 294-310).
Springer DOI
1810
BibRef
Bulat, A.[Adrian],
Yang, J.[Jing],
Tzimiropoulos, G.[Georgios],
To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image
Degradation First,
ECCV18(VI: 187-202).
Springer DOI
1810
BibRef
Liu, Z.S.[Zhi-Song],
Wan-Chi, S.[Siu],
Cascaded Random Forests for Fast Image Super-Resolution,
ICIP18(2531-2535)
IEEE DOI
1809
Image resolution, Decision trees, Mathematical model,
Feature extraction, Image reconstruction, Signal resolution,
machine learning
BibRef
Liling, Z.,
Zelin, Z.,
Quansen, S.,
Deep Learning Based Super Resolution Using Significant and General
Regions,
ICIP18(2516-2520)
IEEE DOI
1809
Training, Image resolution, Image reconstruction,
Feature extraction, Roads, Machine learning, Big Data, deep learning,
significant regions
BibRef
Sugawara, Y.,
Shiota, S.,
Kiya, H.,
Super-Resolution Using Convolutional Neural Networks Without Any
Checkerboard Artifacts,
ICIP18(66-70)
IEEE DOI
1809
Convolution, Deconvolution, Training, Linear systems, Steady-state,
Image resolution, Kernel, Super-Resolution, Checkerboard Artifacts
BibRef
Xu, J.,
Chae, Y.,
Stenger, B.,
Datta, A.,
Dense Bynet: Residual Dense Network for Image Super Resolution,
ICIP18(71-75)
IEEE DOI
1809
Convolutional codes, Training, Spatial resolution, Task analysis,
Benchmark testing, Network architecture, image super resolution,
image enhancement
BibRef
Wang, Q.,
Fan, H.,
Cong, Y.,
Tang, Y.,
Large receptive field convolutional neural network for image
super-resolution,
ICIP17(958-962)
IEEE DOI
1803
Convolution, Convolutional neural networks, Feature extraction,
Kernel, Spatial resolution, Training, Convolutional neural network,
Super resolution
BibRef
Liu, Y.,
Chen, Q.,
Wassell, I.,
Deep network for image super-resolution with a dictionary learning
layer,
ICIP17(967-971)
IEEE DOI
1803
Computer architecture, DH-HEMTs, Dictionaries, Encoding,
Image reconstruction, Image resolution, Machine learning,
Super-resolution
BibRef
Xu, J.,
Chae, Y.,
Stenger, B.,
BYNET-SR: Image super resolution with a bypass connection network,
ICIP17(4053-4057)
IEEE DOI
1803
Convergence, Convolution, Image resolution, Mathematical model,
Signal resolution, Task analysis, Training,
super resolution
BibRef
Fan, R.,
Li, S.,
Lei, G.,
Yue, G.,
Shallow and deep convolutional networks for image super-resolution,
ICIP17(1847-1851)
IEEE DOI
1803
Convolution, Feature extraction, Image resolution,
Image restoration, Interpolation, Training, Videos, Super-resolution,
multi-scale manner
BibRef
Bao, W.B.[Wen-Bo],
Zhang, X.Y.[Xiao-Yun],
Yan, S.P.[Shang-Peng],
Gao, Z.Y.[Zhi-Yong],
Iterative convolutional neural network for noisy image
super-resolution,
ICIP17(4038-4042)
IEEE DOI
1803
Convolutional neural networks, Image reconstruction,
Noise measurement, Noise reduction, Spatial resolution, Training,
super-resolution
BibRef
Zhou, L.[Liguo],
Wang, Z.Y.[Zhong-Yuan],
Wang, S.[Shu],
Luo, Y.M.[Yi-Min],
Coarse-to-Fine Image Super-Resolution Using Convolutional Neural
Networks,
MMMod18(II:73-81).
Springer DOI
1802
BibRef
Lai, W.S.,
Huang, J.B.,
Ahuja, N.,
Yang, M.H.,
Deep Laplacian Pyramid Networks for Fast and Accurate
Super-Resolution,
CVPR17(5835-5843)
IEEE DOI
1711
Convolution, Feature extraction, Image reconstruction,
Laplace equations, Spatial resolution, Training
BibRef
Ren, H.,
El-Khamy, M.,
Lee, J.,
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural
Networks for Image Super Resolution,
WACV18(1423-1431)
IEEE DOI
1806
BibRef
Earlier:
Image Super Resolution Based on Fusing Multiple Convolution Neural
Networks,
NTIRE17(1050-1057)
IEEE DOI
1709
feedforward neural nets, image resolution,
learning (artificial intelligence), CT-SRCNN, SR efficiency,
Tuning.
Convolution, Kernel, Neural networks,
Training.
BibRef
Cui, Z.[Zhen],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
Zhong, B.[Bineng],
Chen, X.L.[Xi-Lin],
Deep Network Cascade for Image Super-resolution,
ECCV14(V: 49-64).
Springer DOI
1408
BibRef
Ma, L.[Lin],
Zhang, Y.H.[Yong-Hua],
Lu, Y.[Yan],
Wu, F.[Feng],
Zhao, D.B.[De-Bin],
Three-tiered network model for image hallucination,
ICIP08(357-360).
IEEE DOI
0810
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Registration for Super Resolution .