🤖 AI Summary
To address insufficient frequency-domain modeling and global structural distortions caused by pixel-wise losses in low-light image enhancement, this paper proposes a distribution-aware Fourier-domain optimization framework. Methodologically: (1) a closed-form Fourier-domain loss based on KL divergence is designed to directly align the statistical distributions of amplitude and phase spectra between enhanced and ground-truth images; (2) a U-shaped network integrating cross-attention and gating mechanisms is introduced to jointly model spatial-frequency dependencies; (3) KL divergence is embedded into the VGG perceptual loss to enhance deep semantic structure fidelity. Extensive experiments demonstrate state-of-the-art performance across multiple benchmark datasets, with both quantitative metrics and qualitative visual results significantly surpassing existing methods. The source code is publicly available.
📝 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