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Open Access

MAGAN: Unsupervised Low-Light Image Enhancement Guided by Mixed-Attention

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410006, China
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Abstract

Most learning-based low-light image enhancement methods typically suffer from two problems. First, they require a large amount of paired data for training, which are difficult to acquire in most cases. Second, in the process of enhancement, image noise is difficult to be removed and may even be amplified. In other words, performing denoising and illumination enhancement at the same time is difficult. As an alternative to supervised learning strategies that use a large amount of paired data, as presented in previous work, this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion. We introduce a mixed-attention module layer, which can model the relationship between each pixel and feature of the image. In this way, our network can enhance a low-light image and remove its noise simultaneously. In addition, we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.

Keywords

low-light image enhancementunsupervised learningGenerative Adversarial Network (GAN)mixed-attention

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Big Data Mining and Analytics
Volume 5 Issue 2,
June 2022
Pages 110-119
DOI: 10.26599/BDMA.2021.9020020
Cite this article:
Wang R, Jiang B, Yang C, et al. MAGAN: Unsupervised Low-Light Image Enhancement Guided by Mixed-Attention. Big Data Mining and Analytics, 2022, 5(2): 110-119. https://doi.org/10.26599/BDMA.2021.9020020

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Received: 15 October 2021
Accepted: 03 November 2021
Published: 25 January 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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