激光与光电子学进展, 2019, 56 (23): 231010, 网络出版: 2019-11-27   

双判别器生成对抗网络图像的超分辨率重建方法 下载: 1331次

Image Super-Resolution Reconstruction Method Using Dual Discriminator Based on Generative Adversarial Networks
作者单位
云南师范大学信息学院, 云南 昆明 650500
引用该论文

袁飘逸, 张亚萍. 双判别器生成对抗网络图像的超分辨率重建方法[J]. 激光与光电子学进展, 2019, 56(23): 231010.

Piaoyi Yuan, Yaping Zhang. Image Super-Resolution Reconstruction Method Using Dual Discriminator Based on Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231010.

参考文献

[1] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.

[2] Subhasis C. Super-resolution imaging[M]. The springer international series in engineering and computer science. New York: Springer Science+Business Media, 2001, 632: XIV, 280.

[3] DongC, Loy CC, He KM, et al. Learning a deep convolutional network for image super-resolution[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 2014, 8692: 184- 199.

[4] KimJ, Lee JK, Lee KM. Deeply-recursive convolutional network for image super-resolution[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada. New York: IEEE, 2016: 1637- 1645.

[5] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. ( 2015-04-10)[2019-05-12]. https:∥arxiv.org/abs/1409. 1556.

[6] Shi WZ, CaballeroJ, HuszarF, 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. New York: IEEE, 2016: 1874- 1883.

[7] LimB, SonS, KimH, 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. New York: IEEE, 2017: 1132- 1140.

[8] Lai WS, Huang JB, AhujaN, 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. New York: IEEE, 2017: 5835- 5843.

[9] Sønderby CK, CaballeroJ, TheisL, et al. Amortised MAP inference for image super-resolution[J/OL]. ( 2017-02-21)[2019-05-12]. https:∥arxiv.org/abs/1610. 04490.

[10] DahlR, NorouziM, ShlensJ. Pixel recursive super resolution[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 5439- 5448.

[11] Goodfellow IJ, Pouget-AbadieJ, MirzaM, et al. Generative adversarial nets[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS, 2017.

[12] LedigC, TheisL, HuszarF, 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. New York: IEEE, 2017: 4681- 4690.

[13] Wang XT, YuK, Wu SX, 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. Cham: Springer, 2019, 11133: 63- 79.

[14] Nguyen TD, LeT, VuH, et al. Dual discriminator generative adversarial nets[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS, 2017.

[15] He KM, Zhang XY, Ren SQ, et al. Identity mappings in deep residual networks[M] ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 2016, 9908: 630- 645.

[16] GulrajaniI, AhmedF, ArjovskyM, et al. Improved training of Wasserstein GANs[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. Canada: NIPS, 2017.

[17] Wang Z, Bovik A C. Mean squared error: love it or leave it? A new look at signal fidelity measures[J]. IEEE Signal Processing Magazine, 2009, 26(1): 98-117.

[18] Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 47-57.

[19] Bruhn A, Weickert J, Schnörr C. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods[J]. International Journal of Computer Vision, 2005, 61(3): 211-231.

[20] Wu BZ, Duan HD, Liu ZC, et al. SRPGAN: perceptual generative adversarial network for single image super resolution[J/OL]. ( 2017-12-20)[2019-05-12]. https:∥arxiv.org/abs/1712. 05927.

[21] Liu GL, Reda FA, Shih KJ, et al. Image inpainting for irregular holes using partial convolutions[M] ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 2018, 11215: 89- 105.

[22] 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), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1122- 1131.

[23] BevilacquaM, RoumyA, GuillemotC, et al. Neighbor embedding based single-image super-resolution using Semi-Nonnegative Matrix Factorization[C]∥2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 25-30, 2012, Kyoto, Japan. New York: IEEE, 2012: 1289- 1292.

[24] YuanY, Liu SY, Zhang JW, et al. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks[C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE, 2018: 814- 823.

[25] MartinD, FowlkesC, TalD, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]∥Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, July 7-14, 2001, Vancouver, BC, Canada. New York: IEEE, 2001: 416- 423.

[26] Zhang YL, Tian YP, KongY, et al. Residual dense network for image restoration[J/OL]. ( 2018-12-25)[2019-05-12]. https:∥arxiv.org/abs/1812. 10477.

袁飘逸, 张亚萍. 双判别器生成对抗网络图像的超分辨率重建方法[J]. 激光与光电子学进展, 2019, 56(23): 231010. Piaoyi Yuan, Yaping Zhang. Image Super-Resolution Reconstruction Method Using Dual Discriminator Based on Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231010.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!