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非可信智能驱动的可靠智造
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Conference Articles
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Region-Adaptive Low-Light Image Enhancement with Light Effect Suppression and Detail Preservation
Liheng Luo,
Xie Wantong,
Xiushan Xia,
Zerui Li,
and Yun-Bo Zhao
In Pacific Graphics Conference Papers, Posters, and Demos
2025
[Abs]
[doi]
[pdf]
Low-light image enhancement seeks to improve the visual quality of images captured under poor illumination, yet existing methods often struggle with unnatural artifacts, overexposure, or detail loss, particularly in challenging real-world scenarios like underground coal mines. We propose a novel unsupervised region-adaptive framework that integrates light effect suppression and detail preservation to address these issues. Leveraging Retinex theory, our approach decomposes images into illumination and reflectance components, employing a region segmentation module to distinguish dark and bright areas for targeted enhancement. A lightweight denoising network mitigates noise, while an adaptive illumination enhancer and light effect suppressor collaboratively optimize illumination to ensure natural appearance and correct visual imbalances. A composite loss function balances brightness enhancement, structural integrity, and artifact suppression across regions. Extensive experiments on the LOL-v2, LSRW and our private datasets demonstrate superior performance. For instance, on our dataset, improvements of 3.26% in BRISQUE, 0.24% in NIQE, and 11.22% in PIQE were achieved compared to state-of-the-art methods, providing visually pleasing results with enhanced brightness, reduced artifacts, and preserved textures, making it well-suited for real-world applications.
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