Using KL-Divergence to Focus Frequency Information in Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2509.13083v2
• Date: Thu, 02 Oct 2025 07:14:11 GMT
• Title: Using KL-Divergence to Focus Frequency Information in Low-Light Image Enhancement
• Authors: Yan Xingyang, Huang Xiaohong, Zhang Zhao, You Tian, Xu Ziheng,
• Abstract summary: LLFDisc is a U-shaped deep enhancement network that integrates cross-attention and gating mechanisms tailored for frequency-aware enhancement.<n>We propose a novel distribution-aware loss that directly fits the Fourier-domain information and minimizes their divergence using a closed-form KL-Divergence objective.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the Fourier domain, luminance information is primarily encoded in the amplitude spectrum, while spatial structures are captured in the phase components. The traditional Fourier Frequency information fitting employs pixel-wise loss functions, which tend to focus excessively on local information and may lead to global information loss. In this paper, we present LLFDisc, a U-shaped deep enhancement network that integrates cross-attention and gating mechanisms tailored for frequency-aware enhancement. We propose a novel distribution-aware loss that directly fits the Fourier-domain information and minimizes their divergence using a closed-form KL-Divergence objective. This enables the model to align Fourier-domain information more robustly than with conventional MSE-based losses. Furthermore, we enhance the perceptual loss based on VGG by embedding KL-Divergence on extracted deep features, enabling better structural fidelity. Extensive experiments across multiple benchmarks demonstrate that LLFDisc achieves state-of-the-art performance in both qualitative and quantitative evaluations. Our code will be released at: https://github.com/YanXY000/LLFDisc

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