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Semi-Supervised Learning for Low-light Image Restoration through Quality Assisted Pseudo-Labeling (WACV'2023)

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Abstract

Convolutional neural networks have been successful in restoring images captured under poor illumination conditions. 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.

If you find the resource useful, please cite the following :- )

@InProceedings{Malik_2023_WACV,
    author    = {Malik, Sameer and Soundararajan, Rajiv},
    title     = {Semi-Supervised Learning for Low-Light Image Restoration Through Quality Assisted Pseudo-Labeling},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {4105-4114}
}