Single image super-resolution: a comprehensive review and recent insight

[1]  Dengwen Zhou,et al.  A lightweight multi-scale channel attention network for image super-resolution , 2021, Neurocomputing.

[2]  Yihao Liu,et al.  Blind Image Super-Resolution: A Survey and Beyond , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Chunjing Xu,et al.  Data-Free Knowledge Distillation For Image Super-Resolution , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Huanjie Tao,et al.  Attention Mechanisms in CNN-Based Single Image Super-Resolution: A Brief Review and a New Perspective , 2021, Electronics.

[5]  Wei An,et al.  Unsupervised Degradation Representation Learning for Blind Super-Resolution , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Luc Van Gool,et al.  Flow-based Kernel Prior with Application to Blind Super-Resolution , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  S. M. A. Bashir,et al.  A comprehensive review of deep learning-based single image super-resolution , 2021, PeerJ Comput. Sci..

[8]  Xueming Qian,et al.  Kernel-attended residual network for single image super-resolution , 2020, Knowl. Based Syst..

[9]  Tieniu Tan,et al.  Unfolding the Alternating Optimization for Blind Super Resolution , 2020, NeurIPS.

[10]  Tao Dai,et al.  Fakd: Feature-Affinity Based Knowledge Distillation for Efficient Image Super-Resolution , 2020, 2020 IEEE International Conference on Image Processing (ICIP).

[11]  Jie Liu,et al.  Residual Feature Distillation Network for Lightweight Image Super-Resolution , 2020, ECCV Workshops.

[12]  Bumsub Ham,et al.  Learning with Privileged Information for Efficient Image Super-Resolution , 2020, ECCV.

[13]  Jianping Gou,et al.  Knowledge Distillation: A Survey , 2020, International Journal of Computer Vision.

[14]  Jie Tang,et al.  Residual Feature Aggregation Network for Image Super-Resolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Luc Van Gool,et al.  Deep Unfolding Network for Image Super-Resolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Shunta Maeda,et al.  Unpaired Image Super-Resolution Using Pseudo-Supervision , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Kani Chen,et al.  In vivo single-cell lineage tracing in zebrafish using high-resolution infrared laser-mediated gene induction microscopy , 2020, eLife.

[18]  Wen Zhang,et al.  Deep learning methods in real-time image super-resolution: a survey , 2019, Journal of Real-Time Image Processing.

[19]  Olga Sorkine-Hornung,et al.  Blind image super-resolution with spatially variant degradations , 2019, ACM Trans. Graph..

[20]  Lanfeng Zhou,et al.  A Review of Deep Learning for Single Image Super-Resolution , 2019, 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

[21]  Alex Khodaverdian,et al.  Inference of single-cell phylogenies from lineage tracing data using Cassiopeia , 2019, Genome Biology.

[22]  Qijun Zhao,et al.  Efficient Single Image Super-Resolution via Hybrid Residual Feature Learning with Compact Back-Projection Network , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[23]  Chiman Kwan,et al.  Deep Learning for Remote Sensing Image Super-Resolution , 2019, 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[24]  Xinbo Gao,et al.  Lightweight Image Super-Resolution with Information Multi-distillation Network , 2019, ACM Multimedia.

[25]  M. Irani,et al.  Blind Super-Resolution Kernel Estimation using an Internal-GAN , 2019, NeurIPS.

[26]  Yu Qiao,et al.  RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[27]  Qinghua Hu,et al.  Neural Blind Deconvolution Using Deep Priors , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Xinying Xu,et al.  Deep Learning Based Single Image Super-resolution: A Survey , 2019, International Journal of Automation and Computing.

[29]  Jia Guo,et al.  SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks , 2019, Sensors.

[30]  Zhiguo Jiang,et al.  A Comparable Study of CNN-Based Single Image Super-Resolution for Space-Based Imaging Sensors , 2019, Sensors.

[31]  Nick Barnes,et al.  Densely Residual Laplacian Super-Resolution , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Shu-Tao Xia,et al.  Second-Order Attention Network for Single Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Bo Yan,et al.  Deep Objective Quality Assessment Driven Single Image Super-Resolution , 2019, IEEE Transactions on Multimedia.

[34]  Yue Cheng,et al.  Super-resolved image perceptual quality improvement via multifeature discriminators , 2019, J. Electronic Imaging.

[35]  Wangmeng Zuo,et al.  Blind Super-Resolution With Iterative Kernel Correction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Yun Fu,et al.  Residual Non-local Attention Networks for Image Restoration , 2019, ICLR.

[37]  Steven C. H. Hoi,et al.  Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Ping Li,et al.  Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution , 2019, IEEE Transactions on Image Processing.

[39]  Michal Irani,et al.  “Double-DIP”: Unsupervised Image Decomposition via Coupled Deep-Image-Priors , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Tong Tong,et al.  Image Super-Resolution Using Knowledge Distillation , 2018, ACCV.

[41]  Jun-Hyuk Kim,et al.  RAM: Residual Attention Module for Single Image Super-Resolution , 2018, ArXiv.

[42]  Yuan Yao,et al.  Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution , 2018, Remote. Sens..

[43]  K. Hayat.  Multimedia super-resolution via deep learning: A survey , 2018, Digit. Signal Process..

[44]  Jie Li,et al.  Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[45]  Radu Timofte,et al.  2018 PIRM Challenge on Perceptual Image Super-resolution , 2018, ArXiv.

[46]  Kang Kim,et al.  Super-resolution ultrasound imaging method for microvasculature in vivo with a high temporal accuracy , 2018, Scientific Reports.

[47]  Kangfu Mei,et al.  Multi-scale Residual Network for Image Super-Resolution , 2018, ECCV.

[48]  Yu Qiao,et al.  ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.

[49]  Wei Wang,et al.  Deep Learning for Single Image Super-Resolution: A Brief Review , 2018, IEEE Transactions on Multimedia.

[50]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[51]  Alexia Jolicoeur-Martineau,et al.  The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.

[52]  Peng Wang,et al.  Adaptive Importance Learning for Improving Lightweight Image Super-Resolution Network , 2018, International Journal of Computer Vision.

[53]  Xinbo Gao,et al.  Fast and Accurate Single Image Super-Resolution via Information Distillation Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[54]  Kyung-Ah Sohn,et al.  Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network , 2018, ECCV.

[55]  Nassir Navab,et al.  Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks , 2018, MICCAI.

[56]  Gregory Shakhnarovich,et al.  Deep Back-Projection Networks for Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[57]  Zainab Awan,et al.  Image Quality Assessment - A Survey of Recent Approaches , 2018 .

[58]  Yun Fu,et al.  Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[59]  Dipti Patra,et al.  Enhanced Iterative Back-Projection Based Super-Resolution Reconstruction of Digital Images , 2018 .

[60]  Xiang Li,et al.  Mixed Link Networks , 2018, IJCAI.

[61]  Hong Zhu,et al.  Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement , 2018, Sensors.

[62]  Ying Tai,et al.  SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[63]  Michal Irani,et al.  "Zero-Shot" Super-Resolution Using Deep Internal Learning , 2017, CVPR.

[64]  Wangmeng Zuo,et al.  Learning a Single Convolutional Super-Resolution Network for Multiple Degradations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[65]  Y. Blau,et al.  The Perception-Distortion Tradeoff , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[66]  Tong Tong,et al.  Image Super-Resolution Using Dense Skip Connections , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[67]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[68]  Thomas S. Huang,et al.  Computed tomography super-resolution using convolutional neural networks , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[69]  Jae-Seok Choi,et al.  A Deep Convolutional Neural Network with Selection Units for Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[70]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[71]  Jian Yang,et al.  Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[72]  K. S. Vishvaksenan,et al.  Security and resolution enhanced transmission of medical image through IDMA aided coded STTD system , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[73]  Jun Ma,et al.  Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images , 2017, Sensors.

[74]  Neil Patrick Del Gallego,et al.  Multiple-image super-resolution on mobile devices: an image warping approach , 2017, EURASIP J. Image Video Process..

[75]  Jian Shao,et al.  A Super-resolution Reconstruction Algorithm for Surveillance Video , 2017 .

[76]  Chih-Yuan Yang,et al.  Learning a No-Reference Quality Metric for Single-Image Super-Resolution , 2016, Comput. Vis. Image Underst..

[77]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[78]  Amanjot Singh,et al.  Super Resolution Applications in Modern Digital Image Processing , 2016 .

[79]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

[80]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[81]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[82]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[83]  Kyoung Mu Lee,et al.  Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[84]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[85]  T. Lu,et al.  Super-Resolution for Surveillance Video Via Adaptive Block-matching Registration , 2015 .

[86]  Jithin Saji Isaac,et al.  Super resolution techniques for medical image processing , 2015, 2015 International Conference on Technologies for Sustainable Development (ICTSD).

[87]  Paresh V. Virparia,et al.  A Review: Image Interpolation Techniques forImage Scaling , 2015 .

[88]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[89]  M. Orrit.  Celebrating optical nanoscopy , 2014, Nature Photonics.

[90]  Luc Van Gool,et al.  A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution , 2014, ACCV.

[91]  Hatem Magdy Keshk,et al.  Performance evaluation of quality measurement for super-resolution satellite images , 2014, 2014 Science and Information Conference.

[92]  S. Opozda,et al.  The survey of subjective and objective methods for quality assessment of 2D and 3D images , 2014 .

[93]  Sven Haase,et al.  Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging , 2014, GCPR.

[94]  A. Small,et al.  Fluorophore localization algorithms for super-resolution microscopy , 2014, Nature Methods.

[95]  Michal Irani,et al.  Nonparametric Blind Super-resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[96]  Luc Van Gool,et al.  Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[97]  Zhe L. Lin,et al.  Fast Image Super-Resolution Based on In-Place Example Regression , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[98]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[99]  Xiaogang Wang,et al.  Image Transformation Based on Learning Dictionaries across Image Spaces , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[100]  Jency Titus,et al.  A Comparison Study On Different Interpolation Methods Based On Satellite Images , 2013 .

[101]  A. Sporbert,et al.  High-Resolution Intravital Microscopy , 2012, PloS one.

[102]  A. J. Shah,et al.  Image super resolution-A survey , 2012, 2012 1st International Conference on Emerging Technology Trends in Electronics, Communication & Networking.

[103]  Siti Norul Huda Sheikh Abdullah,et al.  Super resolution combination methods for CCTV forensic interpretation , 2012, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).

[104]  Aline Roumy,et al.  Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding , 2012, BMVC.

[105]  Zhou Wang,et al.  Objective quality assessment for image super-resolution: A natural scene statistics approach , 2012, 2012 19th IEEE International Conference on Image Processing.

[106]  Thomas S. Huang,et al.  Coupled Dictionary Training for Image Super-Resolution , 2012, IEEE Transactions on Image Processing.

[107]  Raanan Fattal,et al.  Image and video upscaling from local self-examples , 2011, TOGS.

[108]  Kai-Kuang Ma,et al.  A survey on super-resolution imaging , 2011, Signal Image Video Process..

[109]  Wanggen Wan,et al.  An improved interpolation algorithm using nearest neighbor from VTK , 2010, 2010 International Conference on Audio, Language and Image Processing.

[110]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[111]  Michael Elad,et al.  On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.

[112]  Mei Yu,et al.  Reduced Reference Image Quality Assessment Based on Contourlet Domain and Natural Image Statistics , 2009, 2009 Fifth International Conference on Image and Graphics.

[113]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[114]  Denis Kouame,et al.  Super-resolution in medical imaging : An illustrative approach through ultrasound , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[115]  Yihong Gong,et al.  Visual‐Quality Optimizing Super Resolution , 2009, Comput. Graph. Forum.

[116]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[117]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.

[118]  Hong Chang,et al.  Super-resolution through neighbor embedding , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[119]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[120]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[121]  Hayit Greenspan,et al.  MRI Inter-slice Reconstruction Using Super-Resolution , 2001, MICCAI.

[122]  M. Orchard,et al.  New edge-directed interpolation , 2001, IEEE Trans. Image Process..

[123]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[124]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[125]  Robert L. Stevenson,et al.  A Bayesian approach to image expansion for improved definitio , 1994, IEEE Trans. Image Process..

[126]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[127]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[128]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[129]  H. Ten Have,et al.  Enhancement , 2021, Dictionary of Global Bioethics.

[130]  Yuhang Jiao,et al.  Upsampling Attention Network for Single Image Super-resolution , 2021, VISIGRAPP.

[131]  2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2021 .

[132]  Recent Advances in Computer Vision - Theories and Applications , 2019, Recent Advances in Computer Vision.

[133]  Linda G. Shapiro,et al.  A Review of Computer Vision Segmentation Algorithms , 2012 .

[134]  A. Martins.  Super-Resolution Image Reconstruction using the Discontinuity Adaptive ICM , 2007 .

[135]  A. Bovik.  A VISUAL INFORMATION FIDELITY APPROACH TO VIDEO QUALITY ASSESSMENT , 2005 .

[136]  Andrew Blake,et al.  Super-resolution Enhancement of Video , 2003, AISTATS.

[137]  Daniel Hirst,et al.  Comparison of subjective evaluation and an objective evaluation metric for prosody in text-to-speech synthesis , 1998, SSW.

[138]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[139]  A. Reibman,et al.  Subjective performance evaluation of super-resolution image enhancement , 2022 .

[140]  J. Evain,et al.  Subjective quality evaluation – The SSCQE and DSCQE methodologies , 2022 .