DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image Enhancement
Abstract: In the Fourier frequency domain, luminance information is primarily encoded in the amplitude component, while spatial structure information is significantly contained within the phase component. Existing low-light image enhancement techniques using Fourier transform have mainly focused on amplifying the amplitude component and simply replicating the phase component, an approach that often leads to color distortions and noise issues. In this paper, we propose a Dual-Stage Multi-Branch Fourier Low-Light Image Enhancement (DMFourLLIE) framework to address these limitations by emphasizing the phase component's role in preserving image structure and detail. The first stage integrates structural information from infrared images to enhance the phase component and employs a luminance-attention mechanism in the luminance-chrominance color space to precisely control amplitude enhancement. The second stage combines multi-scale and Fourier convolutional branches for robust image reconstruction, effectively recovering spatial structures and textures. This dual-branch joint optimization process ensures that complex image information is retained, overcoming the limitations of previous methods that neglected the interplay between amplitude and phase. Extensive experiments across multiple datasets demonstrate that DMFourLLIE outperforms current state-of-the-art methods in low-light image enhancement.
Primary Subject Area: [Experience] Multimedia Applications
Relevance To Conference: In this paper, we introduce the Dual-Stage Multi-Branch Fourier Low-Light Image Enhancement (DMFourLLIE) framework, a significant advancement in multimedia and multimodal processing. DMFourLLIE addresses critical challenges in low-light image enhancement, crucial for applications in surveillance, photography, and autonomous driving. Utilizing Fourier frequency information, the framework emphasizes preserving the phase component to maintain image structure and integrity. It integrates structural priors from infrared images and luminance-chrominance representations, enhancing phase and amplitude component accuracy while preventing color distortions and noise. The innovative dual-path structure combines multi-scale and Fourier convolutional techniques, ensuring detailed preservation of spatial textures. This approach not only refines low-light image enhancement techniques but also has broad implications for multimedia processing. The framework's adaptability to other multimodal applications and its potential to guide various enhancement strategies underscore its universal significance. By maintaining the intrinsic relationship between amplitude and phase components, DMFourLLIE fosters new multimedia data processing strategies, enhancing visual representation quality and interpretability. This work propels the state-of-the-art in image enhancement and contributes to the evolution of multimedia data processing technologies.
Submission Number: 2473
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