Semi-Supervised Learning for Low-light Image Restoration through Quality Assisted Pseudo-Labeling

Malik, S and Soundararajan, R (2023) Semi-Supervised Learning for Low-light Image Restoration through Quality Assisted Pseudo-Labeling. In: 23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023, 3 - 7 January 2023, Waikoloa, pp. 4094-4103.

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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. Project page is available at https://github.com/sameerIISc/SSL-LLR. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Color photography; Convolutional neural networks; Learning algorithms; Machine learning; Restoration, Algorithm: computational photography; And algorithm (including transfer, low-shot, semi-, self-, and un-supervised learning); Computational photography; Formulation; Images synthesis; Learning architectures; Machine learning architecture; Machine-learning; Un-supervised learning; Video synthesis, Image reconstruction
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	15 Mar 2023 05:53
Last Modified:	15 Mar 2023 05:53
URI:	https://eprints.iisc.ac.in/id/eprint/80987

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