Semi-Supervised Learning for Low-Light Image Restoration Through Quality Assisted Pseudo-Labeling

Sameer Malik, Rajiv Soundararajan; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4105-4114

Convolutional neural networks have been successful in restoring images captured under poor illumination conditions addressing multiple challenges such as contrast enhancement, denoising, and color cast removal. Nevertheless, such approaches require a large number of paired low-light and ground truth images for training. Thus, we study the problem of semi-supervised learning for low-light image restoration when limited low-light images have ground truth labels. Our main contributions in this work are twofold. We first deploy an ensemble of low-light restoration networks to restore the unlabeled images and generate a set of potential pseudo-labels. We model the contrast distortions in the labeled set to generate different sets of training data and create the ensemble of networks. We then design a contrastive self-supervised learning based image quality measure to obtain the pseudo-label among the images restored by the ensemble. We show that training the restoration network with the pseudo-labels allows us to achieve excellent restoration performance even with very few labeled pairs. We conduct extensive experiments on three popular low-light image restoration datasets to show the superior performance of our semi-supervised low-light image restoration compared to other approaches.