Paper detail

Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network

The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors. However, high-performance networks have been specifically designed for a single degradation factor. In this paper, we tackle a harder problem, restoring a clean image from its degraded version with an unknown degradation factor, subject to the condition that it is one of the known factors. Toward this end, we design a network having multiple pairs of input and output branches and use it in a recurrent fashion such that a different branch pair is used at each of the recurrent paths. We reinforce the shared part of the network with improved components so that it can handle different degradation factors. We also propose a two-step training method for the network, which consists of multi-task learning and finetuning. The experimental results show that the proposed network yields at least comparable or sometimes even better performance on four degradation factors as compared with the best dedicated network for each of the four. We also test it on a further harder task where the input image contains multiple degradation factors that are mixed with unknown mixture ratios, showing that it achieves better performance than the previous state-of-the-art method designed for the task.