标题： RDDM：面向真实世界图像修复的RAW域扩散模型实践 显示英文标题

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

摘要： 我们提出了RAW域扩散模型（RDDM），这是一种端到端的扩散模型，可以直接从传感器RAW数据中恢复照片级真实感图像。 尽管最近的sRGB域扩散方法取得了令人印象深刻的结果，但它们在高保真度和真实生成之间陷入了一个两难境地。 由于这些模型处理的是有损的sRGB输入，并且在许多场景中忽略了传感器RAW图像的可访问性，例如在边缘设备中的图像和视频捕捉，导致性能次优。 RDDM通过直接在RAW域中恢复图像来绕过这一限制，替换了传统的两阶段图像信号处理（ISP）+ IR流程。 然而，将预训练的扩散模型简单地适应到RAW域会遇到分布外（OOD）问题。 为此，我们提出：（1）一个RAW域变分自编码器（RVAE），学习最优潜在表示，（2）一个可微的后期色调处理（PTP）模块，实现RAW和sRGB空间的联合优化。 为了弥补数据集的不足，我们开发了一个可扩展的退化流程，从现有的sRGB数据集中合成RAW低质量-高质量（LQ-HQ）对以进行大规模训练。 此外，我们设计了一个可配置的多拜耳（CMB）LoRA模块，用于处理各种RAW模式，如RGGB、BGGR等。 大量实验表明，RDDM在最先进的sRGB扩散方法上表现出优势，产生了更高保真度的结果，且伪影更少。 显示英文摘要

摘要： 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.