Iterative Learning for Distorted Image Restoration

Abstract

Deep generative networks have achieved great success on distorted image restoration. However, existing deep learning approaches mainly focus on delicate module structure while ignoring the saturation problem. In this paper, we study the influence of different learning schemes on fitting capability and tackle the problem by proposing a novel iterative learning scheme. It accumulates weight importance from past episodes and guides the network to search for the optimal of current episodes based on obtained knowledge. Since public available datasets contain very few distortion types, we also release a new benchmark to explore this task. Extensive experimental evaluations on the benchmarks demonstrate that our learning approach significantly outperforms all other methods and achieves new state-of-the-art results.