[1] [1] Zou W W W, Yuen P C, et al. Very low resolution face recognition problem［J］. IEEE Transactions on Image Processing,2012,21(1):327-340.

[2] [2] Sajjadi M S M, Scholkopf B, Hirsch M, et al. Enhancenet: Single image super-resolution through automated texture synthesis［C］∥ IEEE International Conference on Computer Vision (ICCV). Venice,Italy:IEEE,2017:4501-4510.

[3] [3] Shi W Z, Caballero J, Ledig C, et al. Cardiac image super-resolution with global correspondence using multi-atlas patch-match［C］∥Proceedings of Medical Image Computing and Computer Assisted Intervention Society.Nagoya,Japan:Springer,2013:9-16.

[4] [4] Dong C, Loy C C, He K M, et al. Learning a deep convolutional network for image super-resolution［C］∥Proceedings of the13th European Conference on Computer Vision.Zurich,Switzerland:Springer,2014:184-199.

[5] [5] Kim J, Lee J K, Lee K M, et al. Accurate image super-resolution using very deep convolutional networks［C］∥Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas,USA:IEEE,2016:1646-1654.

[6] [6] Tai Y, Yang J, Liu X M, et al. Image super-resolution via deep recursive residual network［C］∥Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA:IEEE,2017:1063-6919.

[7] [7] Zhang K, Zuo W M, Zhang L, et al. Learning a single convolutional super-resolution network for multiple degradations［C］∥Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City,UT,USA:IEEE,2018:326-3271.

[8] [8] Soh J W, Gu Y P, Jo J H, et al. Natural and realistic single image super-resolution with explicit natural manifold discrimination［C］∥Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA:IEEE,2019:8114-8123.

[9] [9] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network［C］∥Proceedings of European Conference on Computer Vision.Springer:ECCV,2016:391-407.

[11] [11] Hua K L, Lo K H, Wang Y C F, et al. Extended guided filtering for depth map upsampling［J］. IEEE MultiMedia,2016,23(2):72-83.

[12] [12] 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］∥Proceedings of International Conference on Computer Vision (ICCV).Vancouver, BC, Canada:IEEE,2001:416-423.

[14] [14] Tai Y W, Liu S, Brown M S, et al. Super resolution using edge prior and single image detail synthesis［C］∥Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,CA,USA:IEEE,2010:2400-240.

[15] [15] Sun J, Xu Z B, Shum H Y, et al. Gradient profile prior and its applications in image super-resolution and enhancement［J］. IEEE Transactions on Image Processing,IEEE,2011,20(6):1529-1542.

[16] [16] Huang J B, Singh A, Ahuja N. Single image superresolution from transformed self-exemplars［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA,USA:IEEE,2015:5197-5206.

[17] [17] Xie J, Feris R S, Sun M T. Edge guided single depth image super resolution［J］. IEEE Transactions on Image Processing,2016,25(1):428-43.

[18] [18] Lai W S, Huang J B, Ahuja N, et al. Deep laplacian pyramid networks for fast and accurate super-resolution［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(11):2599-2613.

[19] [19] Ma C, Rao Y M, Cheng Y, et al. Structure-preserving super resolution with gradient guidance［C］∥ Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Seattle, WA, USA:IEEE,2020:7766-7775.

[20] [20] Matsui Y, Ito K, Aramaki Y, et al. Sketch-based manga retrieval using manga109 dataset［J］. Multimedia Tools and Applications,2017,76(20):21811-21838.

[21] [21] Burrus C S, Gopinath R A, Guo H T. Introduction to wavelets and wavelet transforms :a primer［J］. Prentice-Hall. American Journal of Industrial and Business Management,1988,6(11):1-292.

[22] [22] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations［C］∥Proceedings of International Conference on Curves and Surfaces(ICCS),2010:711-730.

[23] [23] Hong M, Xie Y, Li C H, et al. Distilling image dehazing with heterogeneous task imitation［C］∥Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle,WA,USA:IEEE,2020:346-3471.

[24] [24] Zheng H, Gao X B, Yang Y C, et al. Lightweight image super-resolution with information multi-distillation network［C］∥Proceedings of the 27th ACM International Conference on Multimedia. MM:ACM,2019:2024-2032.

[25] [25] Lin X D, Ma Lin, Liu W, et al. Context-gated convolution［C］∥Proceedings of European Conference on Computer Vision. Springer: ECCV,2020:701-718.

[26] [26] Dai Y M, Gieseke F, Oehmcke S, et al. Attentional feature fusion［C］∥Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision(WACV).Waikoloa,HI,USA:IEEE,2021:3560-3569.

[27] [27] Zamir S W, Arora A, Khan S, et al. Multi-stage progressive image restoration［C］∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).ABS:IEEE,2021:1-16.

[28] [28] Zamir S W, Arora A, Khan S, et al. Restormer: efficient transformer for high-resolution image［C］∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). NASA/ADS:IEEE,2022:1-12.

[29] [29] Gray A, Markel J. Digital lattice and ladder fifilter synthesis［J］. IEEE Transactions on Audio and Electroacoustics,1973,21(6):491-500.

[30] [30] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［C］∥Proceedings of Computer Science-Computer Vision and Pattern Recognition. SEMANTIC SCHOLAR:ICLR,2015:1-140.

[31] [31] Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding［C］∥Proceedings of British Machine Vision Conference (BMVC). CiteSeerx:IEEE,2012:35-135.