Adaptive Low Light Enhancement via Joint Global-Local Illumination Adjustment

• URL: http://arxiv.org/abs/2504.00400v1
• Date: Tue, 01 Apr 2025 03:46:28 GMT
• Title: Adaptive Low Light Enhancement via Joint Global-Local Illumination Adjustment
• Authors: Haodian Wang, Yaqi Song,
• Abstract summary: We propose a novel brightness-adaptive enhancement framework to tackle the challenge of local exposure inconsistencies in low-light images.<n>Our method achieves superior quantitative and qualitative results compared to state-of-the-art algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Images captured under real-world low-light conditions face significant challenges due to uneven ambient lighting, making it difficult for existing end-to-end methods to enhance images with a large dynamic range to normal exposure levels. To address the above issue, we propose a novel brightness-adaptive enhancement framework designed to tackle the challenge of local exposure inconsistencies in real-world low-light images. Specifically, our proposed framework comprises two components: the Local Contrast Enhancement Network (LCEN) and the Global Illumination Guidance Network (GIGN). We introduce an early stopping mechanism in the LCEN and design a local discriminative module, which adaptively perceives the contrast of different areas in the image to control the premature termination of the enhancement process for patches with varying exposure levels. Additionally, within the GIGN, we design a global attention guidance module that effectively models global illumination by capturing long-range dependencies and contextual information within the image, which guides the local contrast enhancement network to significantly improve brightness across different regions. Finally, in order to coordinate the LCEN and GIGN, we design a novel training strategy to facilitate the training process. Experiments on multiple datasets demonstrate that our method achieves superior quantitative and qualitative results compared to state-of-the-art algorithms.

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