Deep Multi-Scale Gabor Wavelet Network for Image Restoration

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multiscale Gabor Wavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse- to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor Wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in- one. We evaluate the proposed method on image demoirding, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.