Unsupervised Image Restoration With Quality-Task-Perception Loss

Abstract

Image restoration includes various kinds of tasks, such as image denoising, image deraining and low-light image enhancement, etc. Due to the domain shift problem of current supervised methods, researchers tend to adopt unsupervised image restoration methods. However, fake color or blur image, insufficient restoration and missing semantic information are three common problems when utilizing these methods. In this paper, we propose a new hybrid loss named Quality-Task-Perception (QTP) to deal with these three problems simultaneously. Specifically, this hybrid loss includes three components: quality, task and perception. The quality part overcomes the fake color or blur image problem by enforcing image quality scores of the restored images and those of the unpaired clean images to be similar. For the task part, we tackle the insufficient restoration problem by proposing to apply a task probability network to convert the unsupervised image restoration into a supervised classification problem, and this task probability network is learned from our proposed pipeline. The perception part handles the missing semantic information by restricting the multi-scale phase consistency between the degraded image and its restored version. Comprehensive experiments on both supervised and unsupervised datasets in three image restoration tasks demonstrate the superiority of our proposed approach.