19.4.3.9 Super Resolution Analysis Using Edges, Edge Analysis for Superresolution

Chapter Contents (Back)
Super Resolution. Edges.

Zhang, D.[Di], Li, H.F.[Hui-Fang], Du, M.H.[Ming-Hui],
Fast MAP-based multiframe super-resolution image reconstruction,
IVC(23), No. 7, 1 July 2005, pp. 671-679.
Elsevier DOI 0506

See also Morphable model space based face super-resolution reconstruction and recognition. BibRef

Zhang, D.[Di], Du, M.H.[Ming-Hui],
High-resolution image reconstruction using joint constrained edge pattern recognition and POCS formulation,
ICARCV04(II: 832-837).
IEEE DOI 0412
BibRef

Yuan, Q.Q.[Qiang-Qiang], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model,
CirSysVideo(22), No. 3, March 2012, pp. 379-392.
IEEE DOI 1203

See also Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties. BibRef

Guo, X.[Xian], Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei],
Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition,
PandRS(83), No. 1, 2013, pp. 50-63.
Elsevier DOI 1308
Image Restoration. Tensor decomposition
See also modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images, A. BibRef

Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng], Yuan, Q.,
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery,
GeoRS(52), No. 8, August 2014, pp. 4729-4743.
IEEE DOI 1403
Gaussian noise BibRef

Zhang, H.Y.[Hong-Yan], Cai, J.Y.[Jing-Yi], He, W.[Wei], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei],
Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping,
GeoRS(60), 2022, pp. 1-19.
IEEE DOI 2112
Noise reduction, Matrix decomposition, Sparse matrices, Gaussian noise, TV, Data models, Solid modeling, Denoising, destriping, low-rank constraint BibRef

He, W.[Wei], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration,
GeoRS(54), No. 1, January 2016, pp. 178-188.
IEEE DOI 1601
hyperspectral imaging BibRef

Zheng, Y.B.[Yu-Bang], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Chen, Y.[Yong], He, W.[Wei],
Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image,
GeoRS(58), No. 12, December 2020, pp. 8450-8464.
IEEE DOI 2012
Image restoration, Tensors, Gray-scale, Principal component analysis, Matrix decomposition, proximal alternating minimization (PAM)
See also Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration. BibRef

He, W.[Wei], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(55), No. 7, July 2017, pp. 3909-3921.
IEEE DOI 1706
Adaptation models, Hyperspectral imaging, Minimization, Robustness, Sparse matrices, TV, Blind unmixing, hyperspectral image, nonnegative matrix factorization (NMF), reweighted sparsity, total, variation, (TV) BibRef

Chen, Y.[Yong], Huang, T.Z.[Ting-Zhu], He, W.[Wei], Yokoya, N.[Naoto], Zhao, X.L.[Xi-Le],
Hyperspectral Image Compressive Sensing Reconstruction Using Subspace-Based Nonlocal Tensor Ring Decomposition,
IP(29), 2020, pp. 6813-6828.
IEEE DOI 2007
Tensile stress, Correlation, Image coding, Image reconstruction, TV, Computational efficiency, Dictionaries, Compressive sensing, tensor ring decomposition BibRef

Chen, Y.[Yong], He, W.[Wei], Yokoya, N.[Naoto], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le],
Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising,
GeoRS(58), No. 2, February 2020, pp. 1348-1362.
IEEE DOI 2001
Noise reduction, Correlation, Matrix decomposition, Hyperspectral imaging, Data models, Denoising, tensor-ring (TR) decomposition BibRef

He, W.[Wei], Yokoya, N.[Naoto], Yuan, L.H.[Long-Hao], Zhao, Q.B.[Qi-Bin],
Remote Sensing Image Reconstruction Using Tensor Ring Completion and Total Variation,
GeoRS(57), No. 11, November 2019, pp. 8998-9009.
IEEE DOI 1911
Image reconstruction, Matrix decomposition, TV, Remote sensing, Cloud computing, MODIS, Cloud removal, gap filling, reconstruction, total variation (TV) BibRef

Zhang, H.Y.[Hong-Yan], Liu, L.[Lu], He, W.[Wei], Zhang, L.P.[Liang-Pei],
Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition,
GeoRS(58), No. 5, May 2020, pp. 3071-3084.
IEEE DOI 2005
Noise reduction, TV, Hyperspectral imaging, Image restoration, Gaussian noise, Denoising, Hyperspectral image (HSI), tensor decomposition BibRef

Zhang, H.Y.[Hong-Yan], Chen, H.Y.[Hong-Yu], Yang, G.Y.[Guang-Yi], Zhang, L.P.[Liang-Pei],
LR-Net: Low-Rank Spatial-Spectral Network for Hyperspectral Image Denoising,
IP(30), 2021, pp. 8743-8758.
IEEE DOI 2111
Noise reduction, Convolution, Image reconstruction, Hyperspectral imaging, Feature extraction, multi-scale features BibRef

Yuan, Q.Q.[Qiang-Qiang], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Hyperspectral Image Denoising Employing a Spectral-Spatial Adaptive Total Variation Model,
GeoRS(50), No. 10, October 2012, pp. 3660-3677.
IEEE DOI 1210
BibRef

Shi, Q.[Qian], Tang, X.P.[Xiao-Pei], Yang, T.[Taoru], Liu, R.[Rong], Zhang, L.P.[Liang-Pei],
Hyperspectral Image Denoising Using a 3-D Attention Denoising Network,
GeoRS(59), No. 12, December 2021, pp. 10348-10363.
IEEE DOI 2112
Noise reduction, Feature extraction, Convolution, Task analysis, Correlation, Kernel, Noise measurement, Atrous convolution, self-attention BibRef

Yuan, Q.Q.[Qiang-Qiang], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Hyperspectral Image Denoising With a Spatial-Spectral View Fusion Strategy,
GeoRS(52), No. 5, May 2014, pp. 2314-2325.
IEEE DOI 1403
Adaptation models BibRef

Miao, Y.C.[Yu-Chun], Zhang, L.[Lefei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng],
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration,
ICCV23(12052-12062)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yuan, Q.Q.[Qiang-Qiang], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Regional Spatially Adaptive Total Variation Super-Resolution with Spatial Information Filtering and Clustering,
IP(22), No. 6, 2013, pp. 2327-2342.
IEEE DOI 1307
k-means clustering, Image edge detection BibRef

Li, J.[Jie], Yuan, Q.Q.[Qiang-Qiang], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei],
Noise Removal From Hyperspectral Image With Joint Spectral-Spatial Distributed Sparse Representation,
GeoRS(54), No. 9, September 2016, pp. 5425-5439.
IEEE DOI 1609
compressed sensing BibRef

Yuan, Q.Q.[Qiang-Qiang], Zhang, Q.[Qiang], Li, J.[Jie], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei],
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network,
GeoRS(57), No. 2, February 2019, pp. 1205-1218.
IEEE DOI 1901
Noise reduction, Feature extraction, Noise measurement, Hyperspectral imaging, Image denoising, multiscale feature extraction BibRef

Zhang, Q.[Qiang], Yuan, Q.Q.[Qiang-Qiang], Li, J.[Jie], Liu, X.X.[Xin-Xin], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei],
Hybrid Noise Removal in Hyperspectral Imagery with a Spatial-Spectral Gradient Network,
GeoRS(57), No. 10, October 2019, pp. 7317-7329.
IEEE DOI 1910
convolutional neural nets, feature extraction, geophysical image processing, gradient methods, spatial-spectral BibRef

Chan, T.M.[Tak-Ming], Zhang, J.P.[Jun-Ping], Pu, J.[Jian], Huang, H.[Hua],
Neighbor embedding based super-resolution algorithm through edge detection and feature selection,
PRL(30), No. 5, 1 April 2009, pp. 494-502.
Elsevier DOI 0903
BibRef
Earlier: A1, A2, Only:
An Improved Super-Resolution with Manifold Learning and Histogram Matching,
ICB06(756-762).
Springer DOI 0601
Super-resolution, Neighbor embedding, Feature selection, Image processing BibRef

Pu, J.[Jian], Zhang, J.P.[Jun-Ping], Guo, P.H.[Pei-Hong], Yuan, X.R.[Xiao-Ru],
Interactive Super-Resolution through Neighbor Embedding,
ACCV09(III: 496-505).
Springer DOI 0909
BibRef

Purkait, P.[Pulak], Chanda, B.[Bhabatosh],
Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization,
IP(21), No. 9, September 2012, pp. 4029-4039.
IEEE DOI 1208
BibRef

Purkait, P.[Pulak], Chanda, B.[Bhabatosh],
Morphologic gain-controlled regularization for edge-preserving super-resolution image reconstruction,
SIViP(7), No. 5, September 2013, pp. 925-938.
WWW Link. 1309
BibRef

Purkait, P.[Pulak], Chanda, B.[Bhabatosh],
Image Upscaling Using Multiple Dictionaries of Natural Image Patches,
ACCV12(III:284-295).
Springer DOI 1304
BibRef

Wang, L., Xiang, S., Meng, G., Wu, H., Pan, C.,
Edge-Directed Single-Image Super-Resolution Via Adaptive Gradient Magnitude Self-Interpolation,
CirSysVideo(23), No. 8, 2013, pp. 1289-1299.
IEEE DOI 1308
Estimation BibRef

Turgay, E.[Emre], Akar, G.B.[Gozde B.],
Texture and edge preserving multiframe super-resolution,
IET-IPR(8), No. 9, September 2014, pp. 499-508.
DOI Link 1410
BibRef
Earlier:
Texture preserving multi frame super resolution with spatially varying image prior,
ICIP12(2205-2208).
IEEE DOI 1302
BibRef
Earlier:
Context based super resolution image reconstruction,
LNLA09(54-61).
IEEE DOI 0908
Gabor filters BibRef

Mosleh, A.[Ali], Bouguila, N.[Nizar], Ben Hamza, A.,
Image and video spatial super-resolution via bandlet-based sparsity regularization and structure tensor,
SP:IC(30), No. 1, 2015, pp. 137-146.
Elsevier DOI 1412
Bandlets
See also Bandlet-based sparsity regularization in video inpainting. BibRef

Yan, Q.[Qing], Xu, Y.[Yi], Yang, X.K.[Xiao-Kang], Nguyen, T.Q.,
Single Image Superresolution Based on Gradient Profile Sharpness,
IP(24), No. 10, October 2015, pp. 3187-3202.
IEEE DOI 1507
Global Positioning System BibRef

Yan, Q.[Qing], Xu, Y.[Yi], Yang, X.K.[Xiao-Kang], Chen, K.[Kai],
Image Super-Resolution Based on a Novel Edge Sharpness Prior,
ICPR12(1056-1059).
WWW Link. 1302
BibRef

Kim, Y., Oh, H., Bilgin, A.,
Super resolution reconstruction based on block matching and three-dimensional filtering with sharpening,
IET-IPR(9), No. 12, 2015, pp. 1048-1056.
DOI Link 1512
adaptive filters BibRef

Yeganli, F., Nazzal, M., Ozkaramanli, H.,
Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness and gradient phase angle,
SIViP(9), No. 1 Supp, December 2015, pp. 285-293.
WWW Link. 1601
BibRef

Yeganli, F., Nazzal, M., Unal, M., Ozkaramanli, H.,
Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness,
SIViP(10), No. 3, March 2016, pp. 535-542.
WWW Link. 1602
BibRef

Singh, A.[Abhishek], Ahuja, N.[Narendra],
Learning ramp transformation for single image super-resolution,
CVIU(135), No. 1, 2015, pp. 109-125.
Elsevier DOI 1504
BibRef
Earlier:
Single image super-resolution using adaptive domain transformation,
ICIP13(947-951)
IEEE DOI 1402
Super-resolution. Image edge detection BibRef

Singh, A.[Abhishek], Porikli, F.M.[Fatih M.], Ahuja, N.[Narendra],
Super-resolving Noisy Images,
CVPR14(2846-2853)
IEEE DOI 1409
denoising, super-resolution BibRef

Li, X., He, H., Wang, R., Tao, D.,
Single Image Superresolution via Directional Group Sparsity and Directional Features,
IP(24), No. 9, September 2015, pp. 2874-2888.
IEEE DOI 1506
Dictionaries BibRef

Ferreira, J.C., Vural, E., Guillemot, C.[Christine],
Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution,
IP(25), No. 3, March 2016, pp. 1354-1367.
IEEE DOI 1602
Adaptation models BibRef

Mandal, S.[Srimanta], Sao, A.K.[Anil Kumar],
Employing structural and statistical information to learn dictionary(s) for single image super-resolution in sparse domain,
SP:IC(48), No. 1, 2016, pp. 63-80.
Elsevier DOI 1609
BibRef
Earlier:
Edge preserving single image super resolution in sparse environment,
ICIP13(967-971)
IEEE DOI 1402
Dictionaries BibRef
And:
Image deblurring in super-resolution framework,
NCVPRIPG13(1-4)
IEEE DOI 1408
Sparse representation. image enhancement BibRef

Sidike, P.[Paheding], Krieger, E.[Evan], Alom, M.Z.[M. Zahangir], Asari, V.K.[Vijayan K.], Taha, T.[Tarek],
A fast single-image super-resolution via directional edge-guided regularized extreme learning regression,
SIViP(11), No. 5, July 2017, pp. 961-968.
Springer DOI 1706
BibRef

Vassilo, K.[Kyle], Heatwole, C.[Cory], Taha, T.[Tarek], Mehmood, A.[Asif],
Multi-Step Reinforcement Learning for Single Image Super-Resolution,
NTIRE20(2160-2168)
IEEE DOI 2008
Image resolution, Image color analysis, Task analysis, Image restoration, Standards, Image reconstruction BibRef

Chen, H., He, X., Qing, L., Teng, Q.,
Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization,
MultMed(19), No. 8, August 2017, pp. 1702-1717.
IEEE DOI 1708
Adaptation models, Estimation, Image edge detection, Image reconstruction, Image resolution, Interpolation, Transforms, Image super-resolution, Split Bregman Iteration, gradient regularization, local structure-adaptive transform, nonlocal, self-similarity BibRef

Mousavi, H.S., Monga, V.,
Sparsity-Based Color Image Super Resolution via Exploiting Cross Channel Constraints,
IP(26), No. 11, November 2017, pp. 5094-5106.
IEEE DOI 1709
BibRef
Earlier:
Sparsity based super resolution using color channel constraints,
ICIP16(579-583)
IEEE DOI 1610
correlation methods, image resolution, optimisation, analogous HR dictionary, color channels, dictionary learning method, edge similarities, image quality metrics, luminance channel information, sparsity-based color image super resolution, Dictionaries, Image color analysis, Image edge detection, Image reconstruction, Spatial resolution, Color super resolution, single-image super resolution, sparse coding BibRef

Yang, W.H.[Wen-Han], Feng, J.S.[Jia-Shi], Yang, J.C.[Jian-Chao], Zhao, F.[Fang], Liu, J.Y.[Jia-Ying], Guo, Z.M.[Zong-Ming], Yan, S.C.[Shui-Cheng],
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution,
IP(26), No. 12, December 2017, pp. 5895-5907.
IEEE DOI 1710
Feature extraction, Image edge detection, Image reconstruction, Image resolution, Signal resolution, Training, Super-resolution, edge guidance, recurrent residual network, sub-band, recovery BibRef

Ahmed, J.[Junaid], Waqas, M.[Muhammad], Ali, S.[Shamshad], Memon, R.A.[Raheel Ahmed], Klette, R.[Reinhard],
Coupled dictionary learning in wavelet domain for Single-Image Super-Resolution,
SIViP(12), No. 3, March 2018, pp. 453-461.
Springer DOI 1804
BibRef
Earlier: A1, A5, Only:
Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution,
ICPR16(3838-3843)
IEEE DOI 1705
Clustering algorithms, Dictionaries, Image reconstruction, Image resolution, Signal resolution, Sparse matrices, Training BibRef

Hou, B., Zhou, K., Jiao, L.,
Adaptive Super-Resolution for Remote Sensing Images Based on Sparse Representation With Global Joint Dictionary Model,
GeoRS(56), No. 4, April 2018, pp. 2312-2327.
IEEE DOI 1804
Adaptation models, Dictionaries, Image edge detection, Image reconstruction, Image resolution, Interpolation, super-resolution (SR) BibRef

Zhao, J.W.[Jian-Wei], Hu, H.P.[He-Ping], Zhou, Z.H.[Zheng-Hua], Cao, F.L.[Fei-Long],
Super-resolution reconstruction: Using non-local structure similarity and edge sharpness dictionary,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1254-1264.
DOI Link 1712
BibRef

Li, K.Q.[Ke-Qiuyin], Cao, F.L.[Fei-Long],
Super-resolution using neighbourhood regression with local structure prior,
SP:IC(72), 2019, pp. 58-68.
Elsevier DOI 1902
Super-resolution, Clustering, Regression, Structure prior BibRef

Feng, T.T.[Tian-Tian], Jiang, P.[Peng], Liu, X.M.[Xiao-Min], Ma, X.Y.[Xin-Yu],
Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images,
RS(15), No. 22, 2023, pp. 5401.
DOI Link 2311
BibRef

Tai, Y.[Ying], Yang, J.[Jian], Liu, X.M.[Xiao-Ming],
Image Super-Resolution via Deep Recursive Residual Network,
CVPR17(2790-2798)
IEEE DOI 1711
Computational modeling, Convolution, Convolutional codes, Image resolution, Image restoration, Neural networks, Training BibRef


Guo, Z.F.[Zhen-Fang], Ye, Y.[Yuyao], Zhao, Y.[Yang], Wang, R.G.[Rong-Gang],
An Acceleration Framework for Super-resolution Network via Region Difficulty Self-adaption,
MMMod21(I:666-677).
Springer DOI 2106
BibRef

Nathan, S.[Sabari], Kansal, P.[Priya],
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution,
PBVS21(4327-4333)
IEEE DOI 2109
BibRef
Earlier: A2, A1:
A Multi-Level Supervision Model: A novel approach for Thermal Image Super Resolution,
PBVS20(426-431)
IEEE DOI 2008
Training, Convolution, Superresolution, Computer architecture, Robustness, Pattern recognition. Spatial resolution, Training, Task analysis, Image edge detection, Convolution BibRef

Zhou, Y., Deng, W., Tong, T., Gao, Q.,
Guided Frequency Separation Network for Real-World SuperResolution,
NTIRE20(1722-1731)
IEEE DOI 2008
Image color analysis, Image edge detection, Image resolution, Generators, Training BibRef

Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.,
Structure-Preserving Super Resolution With Gradient Guidance,
CVPR20(7766-7775)
IEEE DOI 2008
Image resolution, Image edge detection, Distortion, Image reconstruction, Image restoration, Visualization BibRef

Chen, C.[Chang], Xiong, Z.W.[Zhi-Wei], Tian, X.[Xinmei], Wu, F.[Feng],
Deep Boosting for Image Denoising,
ECCV18(XI: 3-19).
Springer DOI 1810
BibRef
Earlier: A1, A3, A4, A2:
UDNet: Up-Down Network for Compact and Efficient Feature Representation in Image Super-Resolution,
CEFR-LCV17(1069-1076)
IEEE DOI 1802
Acceleration, Convolution, Feature extraction, Image reconstruction, Image resolution, Interpolation, Real-time systems BibRef

Tai, Y.[Ying], Yang, J.[Jian], Liu, X.M.[Xiao-Ming], Xu, C.,
MemNet: A Persistent Memory Network for Image Restoration,
ICCV17(4549-4557)
IEEE DOI 1802
image coding, image denoising, image resolution, image restoration, learning (artificial intelligence), neural nets, JPEG deblocking, Transform coding BibRef

Shibata, T., Sato, A.,
Single image super resolution based on content-aware constraint and intensity-order constraint,
MVA17(153-156)
DOI Link 1708
Eigenvalues and eigenfunctions, Image edge detection, Image reconstruction, Image resolution, Organizations, TV, Training BibRef

Wang, R.[Ruxin], Han, C.Y.[Cong-Ying], Li, M.Q.[Ming-Qiang], Guo, T.D.[Tian-De],
Single Image Super-Resolution Reconstruction Based on Edge-Preserving with External and Internal Gradient Prior Knowledge,
NTIRE16(I: 191-205).
Springer DOI 1704
BibRef

Xia, L.Y., Lin, X.X., Liang, Y., Jiang, H.K., Chai, H., Huang, H.H.,
Image Super-Resolution Reconstruction via L1_2 and S1_2 Regularizations,
DICTA16(1-8)
IEEE DOI 1701
Image edge detection BibRef

Chen, Z., Muramatsu, S., Abe, Y.,
Fast image super-resolution via multiple directional transforms,
ICIP16(1434-1438)
IEEE DOI 1610
Image edge detection BibRef

Su, C.[Chang], Tao, L.[Li],
Fast single-image upsampling with relative edge growth rate priors,
ICIP15(3876-3880)
IEEE DOI 1512
Image upsampling BibRef

Krishnan, S.[Shankar], Klosowski, J.T.[James T.],
Guided image upsampling using bitmap tracing,
ICIP13(650-654)
IEEE DOI 1402
Image edge detection BibRef

Xi, H.Q.[Hui-Qin], Xiao, C.B.[Chuang-Bai], Bian, C.X.[Chun-Xiao],
Edge Halo Reduction for Projections onto Convex Sets Super Resolution Image Reconstruction,
DICTA12(1-7).
IEEE DOI 1303
BibRef

Kang, W.S.[Won-Seok], Jeon, J.H.[Jae-Hwan], Lee, E.S.[Eun-Sung], Cho, C.H.[Chang-Hun], Jung, J.H.[Jung-Hoon], Kim, T.C.[Tae-Chan], Katsaggelos, A.K.[Aggelos K.], Paik, J.[Joonki],
Real-time super-resolution for digital zooming using finite kernel-based edge orientation estimation and truncated image restoration,
ICIP13(1311-1315)
IEEE DOI 1402
Estimation BibRef

Jeon, J.H.[Jae-Hwan], Lee, J.H.[Jin-Hee], Lee, E.S.[Eun-Sung], Hayes, M.H.[Monson H.], Paik, J.K.[Joon-Ki],
Regularized adaptive super-resolution using kernel estimation-based edge reconnection and kernel orientation constraints,
ICIP12(2213-2216).
IEEE DOI 1302
BibRef

Fan, Y.Q.[Ya-Qiong], Gan, Z.L.[Zong-Liang], Qiu, Y.[Yiwen], Zhu, X.C.[Xiu-Chang],
Single Image Super Resolution Method Based on Edge Preservation,
ICIG11(394-399).
IEEE DOI 1109
BibRef

Bie, H.X.[Hong Xia], Liu, C.Y.[Chen Yi],
Edge-Directed Sub-Pixel Extraction and Still Image Super-Resolution,
CISP09(1-4).
IEEE DOI 0910
BibRef

Tai, Y.W.[Yu-Wing], Tong, W.S.[Wai-Shun], Tang, C.K.[Chi-Keung],
Perceptually-Inspired and Edge-Directed Color Image Super-Resolution,
CVPR06(II: 1948-1955).
IEEE DOI 0606
BibRef

Jung, C.H.[Chan-Ho], Kim, G.H.[Gyeong-Hwan],
An Iterative Method for Preserving Edges and Reducing Noise in High Resolution Image Reconstruction,
ACCV06(II:325-334).
Springer DOI 0601
Enhance resolution. BibRef

Jin, H.Y.[Hai-Yan], Yang, X.H.[Xiao-Hui], Jiao, L.C.[Li-Cheng], Liu, F.[Fang],
Image Enhancement via Fusion Based on Laplacian Pyramid Directional Filter Banks,
ICIAR05(239-246).
Springer DOI 0509
BibRef

Omrane, N., Palmer, P.,
Super-resolution using the Walsh functions, a new algorithm for image reconstruction,
ICIP03(II: 299-302).
IEEE DOI 0312
BibRef

Kim, H., Jang, J.H., Hong, K.S.,
Edge-enhancing Super-resolution Using Anisotropic Diffusion,
ICIP01(III: 130-133).
IEEE DOI 0108
BibRef

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


Last update:Mar 16, 2024 at 20:36:19