Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2403.02879v2
• Date: Tue, 9 Jul 2024 08:33:07 GMT
• Title: Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement
• Authors: Jinhong He, Minglong Xue, Aoxiang Ning, Chengyun Song,
• Abstract summary: We propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED. It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains. It successfully alleviates the dependence on pairwise training data via zero-reference learning.
• Score: 2.9873893715462185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED. It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains and successfully alleviates the dependence on pairwise training data via zero-reference learning. Specifically, we first design the initial optimization network to preprocess the input image and implement bidirectional constraints between the diffusion model and the initial optimization network through multiple objective functions. Subsequently, the degradation factors of the real-world scene are optimized iteratively to achieve effective light enhancement. In addition, we explore a frequency-domain based and semantically guided appearance reconstruction module that encourages feature alignment of the recovered image at a fine-grained level and satisfies subjective expectations. Finally, extensive experiments demonstrate the superiority of our approach to other state-of-the-art methods and more significant generalization capabilities. We will open the source code upon acceptance of the paper.

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