Degradation Aware Approach to Image Restoration Using Knowledge Distillation

Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as rain-streaks, haze, raindrops and motion blur. We decompose the restoration task into two stages of degradation localization and degraded region-guided restoration, unlike existing methods which directly learn a mapping between the degraded and clean images. Our premise is to use the auxiliary task of degradation mask prediction to guide the restoration process. We demonstrate that the model trained for this auxiliary task contains vital region knowledge, which can be exploited to guide the restoration network's training using attentive knowledge distillation technique. Further, we propose mask-guided gated convolution and global context aggregation module leveraging the extra guidance from the predicted mask while focusing on restoring the degraded regions. We conduct an extensive evaluation on multiple datasets corresponding to four different restoration tasks to validate our method. Along with thorough ablation analysis and visualizations, the proposed approach's effectiveness is also demonstrated by achieving significant improvement over strong baselines for each restoration task.