Jinshan Pan Jiangxin Dong Yang Liu Jiawei Zhang Jimmy Ren Jinhui Tang Yu-Wing Tai Ming-Hsuan Yang
Abstract
We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, image deraining, etc.). These problems are highly ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we find that these problems can be solved by generative models with adversarial learning. However, the basic formulation of generative adversarial networks (GANs) does not generate realistic images, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose a physics model constrained learning algorithm so that it can guide the estimation of the specific task in the conventional GAN framework. The proposed algorithm is trained in an end-to-end fashion and can be applied to a variety of image restoration and related low-level vision problems. Extensive experiments demonstrate that our method performs favorably against state-of-the-art algorithms.
Image Deblurring
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| Blurred image | Hradiš et al. | Ours |
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| Blurred image | DeblurGAN | Ours |
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| Blurred image | Pan et al. | Ours |
Image Dehazing
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| Hazy image | GAN dehazing | Ours |
Image Deraining
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| Rainy image | GAN deraining | Ours |
Proposed Algorithm
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| The proposed framework. The discriminative network $\mathcal{D}_g$ is used to classify whether the distributions of the outputs from the generator $\mathcal{G}$ are close to those of the ground truth images or not. The discriminative network $\mathcal{D}_h$ is used to classify whether the regenerated result $\widetilde{y}_i$ is consistent with the observed image $y_i$ or not. All the networks are jointly trained in an end-to-end manner. |
Visual Comparisons
Image Deblurring
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| Blurred image | Xu et al. | Pan et al. (TPAMI 2017) | Pan et al. (CVPR 2016) |
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| Nah et al. | Hradiš et al. | PCycleGAN | Ours |
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| Blurred image | Xu et al. | Pan et al. (TPAMI 2017) | Pan et al. (ECCV 2014) |
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| Zhang et al. | DeblurGAN | CycleGAN | Ours |
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| Blurred image | Xu et al. | Pan et al. (TPAMI 2017) | Pan et al. (ECCV 2014) |
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| Zhang et al. | DeblurGAN | PCycleGAN | Ours |
Image Dehazing
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| Hazy image | He et al. | Meng et al. | Berman et al. |
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| Chen et al. | Cai et al. | Ren et al. | Ours |
Image Deraining
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| Rainy image | Li et al. | Zhang et al. | Fu et al. |
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| Yang et al. | pix2pix | CycleGAN | Ours |
Technical Papers, Codes, and Datasets
References
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[19] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in ECCV, 2016, pp. 154–169.
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[21] G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in ICCV, 2013, pp. 617–624.
