Cycle-enhance: low-light image enhancement based on CycleGan

Yu Zhou; Yanjie Wang; Wenbing Cai

doi:10.1117/12.2674697

2 May 2023 Cycle-enhance: low-light image enhancement based on CycleGan

Yu Zhou, Yanjie Wang, Wenbing Cai

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420S (2023) https://doi.org/10.1117/12.2674697
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Distortion-free enhancement on images captured under low-light conditions has always been a challenging problem in computer vision. To address these problems, this paper proposes an end-to-end network, which can learn the map way of low-light images to normal-light images from unpaired low-light and normal-light datasets. The network is consisted of dual branches, the upper branch is a refinement branch focusing on noise suppression, and the lower branch is a global reconstruction branch based on light-weight Transformer. The discrimination network adopts the multi-scale discrimination structure of feature pyramid to enhance the global consistency and avoid local overexposure. Qualitative and quantitative experimental results show that the proposed method can effectively suppress the generation of artifacts and noise amplification of enhanced images.

Citation Download Citation

Yu Zhou, Yanjie Wang, and Wenbing Cai "Cycle-enhance: low-light image enhancement based on CycleGan", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420S (2 May 2023); https://doi.org/10.1117/12.2674697

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KEYWORDS

Image enhancement

Denoising

Image processing

Matrices

Image quality

Network architectures

Machine learning

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