Noise Synthesis for Low-Light Image Denoising with Diffusion Models

• URL: http://arxiv.org/abs/2503.11262v1
• Date: Fri, 14 Mar 2025 10:16:54 GMT
• Title: Noise Synthesis for Low-Light Image Denoising with Diffusion Models
• Authors: Liying Lu, Raphaël Achddou, Sabine Süsstrunk,
• Abstract summary: Low-light photography produces images with low signal-to-noise ratios due to limited photons.<n>Deep-learning methods perform well, but they require large datasets of paired images that are impractical to acquire.<n>In this paper, we investigate the ability of diffusion models to capture the complex distribution of low-light noise.
• Score: 22.897202020483576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep-learning methods perform well, they require large datasets of paired images that are impractical to acquire. As a remedy, synthesizing realistic low-light noise has gained significant attention. In this paper, we investigate the ability of diffusion models to capture the complex distribution of low-light noise. We show that a naive application of conventional diffusion models is inadequate for this task and propose three key adaptations that enable high-precision noise generation without calibration or post-processing: a two-branch architecture to better model signal-dependent and signal-independent noise, the incorporation of positional information to capture fixed-pattern noise, and a tailored diffusion noise schedule. Consequently, our model enables the generation of large datasets for training low-light denoising networks, leading to state-of-the-art performance. Through comprehensive analysis, including statistical evaluation and noise decomposition, we provide deeper insights into the characteristics of the generated data.

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