[2] Liu K W, Ma Y, Xiong H X et al. Medical-image super-resolution reconstruction method based on residual channel attention network[J]. Laser & Optoelectronics Progress, 57, 021014(2020).
[3] Zhang H Y, Yang W M, Wang H S. 3D face recognition combining local keypoints with isogeodesic curves[J]. Laser & Optoelectronics Progress, 57, 221503(2020).
[4] Chang J X, Wang S X, Yang Y W et al. Hierarchical optimization method of building contour in high-resolution remote sensing images[J]. Chinese Journal of Lasers, 47, 1010002(2020).
[5] Zhu S Y, Zeng B, Liu G H et al. Image interpolation based on non-local geometric similarities[C]. //2015 IEEE International Conference on Multimedia and Expo (ICME), June 29-July 3, 2015, Turin, Italy., 1-6(2015).
[6] Zhang K B, Gao X B, Tao D C et al. Single image super-resolution with non-local means and steering kernel regression[J]. IEEE Transactions on Image Processing, 21, 4544-4556(2012).
[7] Peng Y F, Gao Y, Du T T et al. Single image super-resolution reconstruction method for generative adversarial network[J]. Journal of Frontiers of Computer Science and Technology, 14, 1612-1620(2020).
[8] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9906, 391-407(2016).
[9] Shi W Z, Caballero J, Huszár F 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), June 27-30, 2016, Las Vegas, NV, USA., 1874-1883(2016).
[10] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).
[11] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1646-1654(2016).
[12] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA., 1132-1140(2017).
[13] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5835-5843(2017).
[14] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 286-301(2018).
[15] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 105-114(2017).
[16] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. //Leal-Taixé L, Roth S. Computer vision-ECCV 2018 Workshops. Lecture notes in computer science, 11133, 63-79(2019).
[17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).
[18] Woo S, Hwang S, Kweon I S. StairNet: top-down semantic aggregation for accurate one shot detection[C]. //2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA, 1093-1102(2018).
[19] Timofte R, Agustsson E, van Gool L et al. Ntire 2017 challenge on single image super-resolution: methods and results[C]. //Proceedings of the IEEE conference on computer vision and pattern recognition workshops, July 21-26, 2017, Honolulu, HI, USA, 114-125(2017).
[20] Lei P C, Liu C, Tang J G et al. Hierarchical feature fusion attention network for image super-resolution reconstruction[J]. Journal of Image and Graphics, 25, 1773-1786(2020).
[21] Yuan Y, Liu S Y, Zhang J W et al. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 18-22, 2018, Salt Lake City, UT, USA., 814-81409(2018).
[22] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 5197-5206(2015).
[23] Matsui Y, Ito K, Aramaki Y et al. Sketch-based manga retrieval using manga109 dataset[J]. Multimedia Tools and Applications, 76, 21811-21838(2017).
