RDDM: Practicing RAW Domain Diffusion Model for Real-world Image Restoration

• URL: http://arxiv.org/abs/2508.19154v1
• Date: Tue, 26 Aug 2025 16:06:17 GMT
• Title: RDDM: Practicing RAW Domain Diffusion Model for Real-world Image Restoration
• Authors: Yan Chen, Yi Wen, Wei Li, Junchao Liu, Yong Guo, Jie Hu, Xinghao Chen,
• Abstract summary: We present an end-to-end diffusion model that restores photo-realistic images directly from the sensor RAW data.<n>We develop a scalable degradation pipeline RAW LQ-HQ pairs from existing sRGB datasets for large-scale training.
• Score: 27.387521556174104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the RAW domain diffusion model (RDDM), an end-to-end diffusion model that restores photo-realistic images directly from the sensor RAW data. While recent sRGB-domain diffusion methods achieve impressive results, they are caught in a dilemma between high fidelity and realistic generation. As these models process lossy sRGB inputs and neglect the accessibility of the sensor RAW images in many scenarios, e.g., in image and video capturing in edge devices, resulting in sub-optimal performance. RDDM bypasses this limitation by directly restoring images in the RAW domain, replacing the conventional two-stage image signal processing (ISP) + IR pipeline. However, a simple adaptation of pre-trained diffusion models to the RAW domain confronts the out-of-distribution (OOD) issues. To this end, we propose: (1) a RAW-domain VAE (RVAE) learning optimal latent representations, (2) a differentiable Post Tone Processing (PTP) module enabling joint RAW and sRGB space optimization. To compensate for the deficiency in the dataset, we develop a scalable degradation pipeline synthesizing RAW LQ-HQ pairs from existing sRGB datasets for large-scale training. Furthermore, we devise a configurable multi-bayer (CMB) LoRA module handling diverse RAW patterns such as RGGB, BGGR, etc. Extensive experiments demonstrate RDDM's superiority over state-of-the-art sRGB diffusion methods, yielding higher fidelity results with fewer artifacts.

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