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Efficient low-light image enhancement with model parameters scaled down to 0.02M

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Abstract

In the field of low-light image enhancement, existing deep learning methods face three significant challenges: inaccurate reflection component estimation, poor image enhancement capabilities, and high computational costs. This study introduces a novel, efficient solution to these problems in the form of an Ultra-Lightweight Enhancement Network (ULENet). Our primary contributions are twofold. First, we propose the combination of channel-wise context mining and spatial-wise reinforcement for improved low-light image enhancement. Second, we introduce a novel lightweight neural architecture, ULENet, designed specifically for this purpose. ULENet features two innovative subnetworks: the channel-wise context mining subnetwork for extracting rich context from low-light images, and the spatial-wise reinforcement subnetwork for extensive spatial feature extraction and detail reconstruction. We use the deep-learning framework PyTorch for training and evaluating our model. Extensive experiments demonstrate that ULENet significantly outperforms nine state-of-the-art low-light enhancement methods in terms of speed, accuracy, and adaptability in complex low-light scenarios. These results validate our initial hypothesis and underscore the effectiveness of the proposed approach.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 62066047, 61966037, and Yunnan Province University Key Laboratory Construction Plan Funding, China.

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  1. School of Information and Engineering, Yunnan University, North Outer Ring Road, Kunming, 100190, Yunnan, China

    Shaoliang Yang & Dongming Zhou

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Yang, S., Zhou, D. Efficient low-light image enhancement with model parameters scaled down to 0.02M. Int. J. Mach. Learn. & Cyber. 15, 1575–1589 (2024). https://doi.org/10.1007/s13042-023-01983-7

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