References
- Tsai R. Multiframe image restoration and registration [J]. Advance Computer Visual and Image Processing, 1984, 1: 317–339.
- 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.
- Huang Jian, Zhao Yuanyuan, Guo Ping, Wang Jing. A review of single image super-resolution reconstruction methods based on deep learning [J]. Computer Engineering and Applications, 2021, 57(18): 13–23.
- Zhang Kaibing, Zhu Danni, Wang Zhen, Yan Yadi. A review of super-resolution image quality evaluation [J]. Computer Engineering and Applications, 2019, 55(04): 31–40+47.
- Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image super-resolution[C]//European conference on computer vision. Springer, Cham, 2014: 184–199.
- Xu Ran, Zhang Junge, Huang Kaiqi. Image super-resolution algorithm using dual-channel convolutional neural network [J]. Journal of Image and Graphics, 2016, 21(5): 9.
- Tang Yanqiu, Pan Hong, Zhu Yaping, Li Xinde. A review of image super-resolution reconstruction research [J]. Chinese Journal of Electronics, 2020, 48(07): 1407–1420.
- Liu Yuefeng, Yang Hanxi, Cai Shuang, Zhang Chenrong. Single image super-resolution reconstruction method based on improved convolutional neural network [J]. Computer Applications, 2019, 39(05): 1440–1447.
- Shi W, Caballero J, F Huszár, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016.
- Xie Haiping, Xie Kaili, Yang Haitao. Research progress of image super-resolution methods [J]. Computer Engineering and Applications, 2020, 56(19):34–41.
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
- Wang Jiaming, Lu Tao. Satellite image super-resolution algorithm based on multi-scale residual deep neural network [J]. Journal of Wuhan Institute of Technology, 2018, 40(04): 440–445.
- Zhang Y, Li K, Li K, et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks[C]// 2018.
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
- Wang X, Yu K, Wu S, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [J]. Springer, Cham, 2018.
- 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.
- Mishiba K, Suzuki T, Ikehara M. Edge-adaptive image interpolation using constrained least squares[C]// IEEE International Conference on Image Processing. IEEE, 2010.
- Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014.
- 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.
- Zhou W, Bovik A C. A universal image quality index [J]. IEEE Signal Processing Letters, 2002, 9(3):81–84.
- 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).
- Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge [J]. International Journal of Computer Vision, 2014:1–42.
- 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.
- Bevilacqua M, Roumy A, Guillemot C, et al. Low-Complexity Single Image Super-Resolution Based on Nonnegative Neighbor Embedding [J]. Bmvc, 2012.
- Zeyde R. On single image scale-up using sparse representation [J]. Curves & Surfaces, 2010.
- 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.
- Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]// IEEE. IEEE, 2015.
- Fujimoto A, Ogawa T, Yamamoto K, et al. Manga109 dataset and creation of metadata[C]// the 1st International Workshop. ACM, 2016.
- 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.
- Chao D, Chen C L, Tang X. Accelerating the Super-Resolution Convolutional Neural Network[C]// European Conference on Computer Vision. Springer International Publishing, 2016.
- Tsai R. Multiframe image restoration and registration [J]. Advance Computer Visual and Image Processing, 1984, 1: 317–339.
- 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.
- 黄健,赵元元,郭苹,王静.深度学习的单幅图像超分辨 率 重 建 方 法 综 述 [J]. 计 算 机 工 程 与 应 用,2021,57(18):13–23.
- 张凯兵,朱丹妮,王珍,闫亚娣.超分辨图像质量评价综 述[J].计算机工程与应用,2019,55(04):31–40+47.
- Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image super-resolution[C]//European conference on computer vision. Springer, Cham, 2014: 184–199.
- 徐冉,张俊格,黄凯奇.利用双通道卷积神经网络的图 像 超 分 辨 率 算 法 [J]. 中 国 图 象 图 形 学 报, 2016, 21(5):9.
- 唐艳秋,潘泓,朱亚平,李新德.图像超分辨率重建研究 综述 [J]. 电子学报, 2020, 48(07):1407–1420.
- 刘月峰,杨涵晰,蔡爽,张晨荣.基于改进卷积神经网络 的 单 幅 图 像 超 分 辨 率 重 建 方 法 [J]. 计算机应 用,2019,39(05):1440–1447.
- Shi W, Caballero J, F Huszár, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016.
- 谢海平,谢凯利,杨海涛.图像超分辨率方法研究进展[J].计算机工程与应用,2020,56(19):34–41.
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
- 汪家明,卢涛.多尺度残差深度神经网络的卫星图像 超 分 辨 率 算 法 [J]. 武 汉 工 程 大 学 学 报, 2018, 40(04):440–445.
- Zhang Y, Li K, Li K, et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks[C]// 2018.
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
- Wang X, Yu K, Wu S, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [J]. Springer, Cham, 2018.
- 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.
- Mishiba K, Suzuki T, Ikehara M. Edge-adaptive image interpolation using constrained least squares[C]// IEEE International Conference on Image Processing. IEEE, 2010.
- Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014.
- 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.
- Zhou W, Bovik A C. A universal image quality index [J]. IEEE Signal Processing Letters, 2002, 9(3):81–84.
- 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).
- Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge [J]. International Journal of Computer Vision, 2014:1–42.
- 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.
- Bevilacqua M, Roumy A, Guillemot C, et al. Low-Complexity Single Image Super-Resolution Based on Nonnegative Neighbor Embedding [J]. bmvc, 2012.
- Zeyde R. On single image scale-up using sparse representation [J]. Curves&Surfaces, 2010.
- 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.
- Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]// IEEE. IEEE, 2015.
- Fujimoto A, Ogawa T, Yamamoto K, et al. Manga109 dataset and creation of metadata[C]// the 1st International Workshop. ACM, 2016.
- 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.
- Chao D, Chen C L, Tang X. Accelerating the Super-Resolution Convolutional Neural Network[C]// European Conference on Computer Vision. Springer International Publishing, 2016.