《百面深度学习》试读 | 系列四：超分辨率重建

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导言

《百面深度学习》自上市以来，获得了众多读者的关注与支持，一直高居京东计算机与互联网类新书榜单的前列，大家的热情是我们精益求精的源源动力。为了更好地与大家进行分享与交流，我们从书中节选了几个关注度比较高的“热门“知识点，重新加以整理，内容涵盖推荐系统、计算广告、自然语言处理、计算机视觉、视频处理、生成式对抗网络等领域的相关知识，供大家试读。

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《百面深度》试读第四篇

[1] DONG C, LOY C C, HE K, 等. Learning a deep convolutional network for image super-resolution[C]//European Conference on Computer Vision. Springer, 2014: 184–199.

[2] KIM J, KWON LEE J, MU LEE K. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1646–1654.

[3] LIM B, SON S, KIM H, 等. Enhanced deep residual networks for single image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017: 136–144.

[4] LEDIG C, THEIS L, HUSZÁR F, 等. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 4681–4690.

[5] ZHANG Y, LI K, LI K, 等. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 286–301.

[6] WANG Z, CHEN J, HOI S C. Deep learning for image super-resolution: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, 2020.

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