Fan Z, Wu H, Fu X, et al. Residual-Guide Feature Fusion Network for Single Image Deraining[J]. arXiv preprint arXiv:1804.07493, 2018.

Zhang K, Zuo W, Zhang L. Learning a single convolutional super-resolution network for multiple degradations[C]//IEEE Conference on Computer Vision and Pattern Recognition. 2018, 6.

Jiao J, Tu W C, He S, et al. Formresnet: Formatted residual learning for image restoration[C]//Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on. IEEE, 2017: 1034-1042.

Keyword [AOD-Net] [MS-SSIM] [SSIM]

Liu Y, Zhao G. PAD-Net: A Perception-Aided Single Image Dehazing Network[J]. arXiv preprint arXiv:1805.03146, 2018.

Keyword [Partial Convolution]

Liu G, Reda F A, Shih K J, et al. Image inpainting for irregular holes using partial convolutions[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 85-100.

Ren W, Ma L, Zhang J, et al. Gated fusion network for single image dehazing[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3253-3261.

Zhang H, Patel V M. Densely connected pyramid dehazing network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3194-3203.

Li B, Peng X, Wang Z, et al. End-to-end united video dehazing and detection[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018.

Zhang H, Patel V M. Density-aware single image de-raining using a multi-stream dense network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 695-704.

Keyword [Locally Adaptive Learning Loss]

Guo J, Ren P, Gu A, et al. Locally Adaptive Learning Loss for Semantic Image Segmentation[J]. arXiv preprint arXiv:1802.08290, 2018.