Deep Likelihood Network for Image Restoration with Multiple Degradation Levels

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Authors: Yiwen Guo,Ming Lu,Wangmeng Zuo,Changshui Zhang,Yurong Chen
ArXiv: 1904.09105
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Abstract URL: https://arxiv.org/abs/1904.09105v3


Convolutional neural networks have been proven very effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and can deteriorate drastically when being applied to some other degradation settings. In this paper, we propose a novel method dubbed deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation settings while keeping their original learning objectives and core architectures. In particular, we slightly modify the original restoration networks by appending a simple yet effective recursive module, which is derived from a fidelity term for disentangling the effect of degradations. Extensive experimental results on image inpainting, interpolation and super-resolution demonstrate the effectiveness of our DL-Net.

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