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Convolutional neural networks have been proven effective in a variety ofimage restoration tasks. Most state-of-the-art solutions, however, are trainedusing images with a single particular degradation level, and their performancedeteriorates drastically when applied to other degradation settings. In thispaper, we propose deep likelihood network (DL-Net), aiming at generalizingoff-the-shelf image restoration networks to succeed over a spectrum ofdegradation levels. We slightly modify an off-the-shelf network by appending asimple recursive module, which is derived from a fidelity term, fordisentangling the computation for multiple degradation levels. Extensiveexperimental results on image inpainting, interpolation, and super-resolutionshow the effectiveness of our DL-Net.

Deep Likelihood Network for Image Restoration with Multiple Degradation Levels