Research on Image Super-resolution Reconstruction Based on Deep Learning

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[1] Tsai R. Multiframe image restoration and registration [J]. Advance Computer Visual and Image Processing, 1984, 1: 317–339. TsaiR Multiframe image restoration and registration [J] Advance Computer Visual and Image Processing 1984 1 317 339 Search in Google Scholar

[2] Viet Khanh Ha, Jin-Chang Ren, Xin-Ying Xu, Sophia Zhao, Gang Xie, Valentin Masero, Amir Hussain. Deep Learning Based Single Image Super-resolution:A Survey [J]. International Journal of Automation and Computing, 2019, 16(04):413–426. HaViet Khanh RenJin-Chang XuXin-Ying ZhaoSophia XieGang MaseroValentin HussainAmir Deep Learning Based Single Image Super-resolution:A Survey [J] International Journal of Automation and Computing 2019 16 04 413 426 Search in Google Scholar

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[15] Wang X, Yu K, Wu S, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [J]. Springer, Cham, 2018. WangX YuK WuS ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [J] Springer Cham 2018 Search in Google Scholar

[16] Johnson J, Alahi A, Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super-Resolution[C]// European Conference on Computer Vision. Springer, Cham, 2016. JohnsonJ AlahiA Fei-FeiL Perceptual Losses for Real-Time Style Transfer and Super-Resolution[C] European Conference on Computer Vision Springer, Cham 2016 Search in Google Scholar

[17] Mishiba K, Suzuki T, Ikehara M. Edge-adaptive image interpolation using constrained least squares[C]// IEEE International Conference on Image Processing. IEEE, 2010. MishibaK SuzukiT IkeharaM Edge-adaptive image interpolation using constrained least squares[C] IEEE International Conference on Image Processing IEEE 2010 Search in Google Scholar

[18] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014. SimonyanK ZissermanA Very Deep Convolutional Networks for Large-Scale Image Recognition [J] Computer Science 2014 Search in Google Scholar

[19] Gatys L A, Ecker A S, Bethge M. Image Style Transfer Using Convolutional Neural Networks[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. GatysL A EckerA S BethgeM Image Style Transfer Using Convolutional Neural Networks[C] 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE 2016 Search in Google Scholar

[20] Zhou W, Bovik A C. A universal image quality index [J]. IEEE Signal Processing Letters, 2002, 9(3):81–84. ZhouW BovikA C A universal image quality index [J] IEEE Signal Processing Letters 2002 9 3 81 84 Search in Google Scholar

[21] Zhou W, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Trans Image Process, 2004, 13(4). ZhouW BovikA C SheikhH R Image quality assessment: from error visibility to structural similarity [J] IEEE Trans Image Process 2004 13 4 Search in Google Scholar

[22] Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge [J]. International Journal of Computer Vision, 2014:1–42. RussakovskyO DengJ SuH ImageNet Large Scale Visual Recognition Challenge [J] International Journal of Computer Vision 2014 1 42 Search in Google Scholar

[23] Agustsson E, Timofte R. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. AgustssonE TimofteR NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study[C] 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) IEEE 2017 Search in Google Scholar

[24] Bevilacqua M, Roumy A, Guillemot C, et al. Low-Complexity Single Image Super-Resolution Based on Nonnegative Neighbor Embedding [J]. bmvc, 2012. BevilacquaM RoumyA GuillemotC Low-Complexity Single Image Super-Resolution Based on Nonnegative Neighbor Embedding [J] bmvc 2012 Search in Google Scholar

[25] Zeyde R. On single image scale-up using sparse representation [J]. Curves&Surfaces, 2010. ZeydeR On single image scale-up using sparse representation [J] Curves & Surfaces 2010 Search in Google Scholar

[26] Martin D, Fowlkes C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]// IEEE International Conference on Computer Vision. IEEE, 2002. MartinD FowlkesC TalD A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C] IEEE International Conference on Computer Vision IEEE 2002 Search in Google Scholar

[27] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]// IEEE. IEEE, 2015. HuangJ B SinghA AhujaN Single image super-resolution from transformed self-exemplars[C] IEEE IEEE 2015 Search in Google Scholar

[28] Fujimoto A, Ogawa T, Yamamoto K, et al. Manga109 dataset and creation of metadata[C]// the 1st International Workshop. ACM, 2016. FujimotoA OgawaT YamamotoK Manga109 dataset and creation of metadata[C] the 1st International Workshop ACM 2016 Search in Google Scholar

[29] Yang, J. Wright, J. Huang, T. Ma, Y. Image Super-Resolution Via Sparse Representation [J]. IEEE Transactions on Image Processing, 2010, 19(11):2861–2873. YangJ. WrightJ. HuangT. MaY. Image Super-Resolution Via Sparse Representation [J] IEEE Transactions on Image Processing 2010 19 11 2861 2873 Search in Google Scholar

[30] Chao D, Chen C L, Tang X. Accelerating the Super-Resolution Convolutional Neural Network[C]// European Conference on Computer Vision. Springer International Publishing, 2016. ChaoD ChenC L TangX Accelerating the Super-Resolution Convolutional Neural Network[C] European Conference on Computer Vision Springer International Publishing 2016 Search in Google Scholar