用扩散模型实现跨相机高保真RGB转RAW，自动适应亮度差异。
SpiralDiff: Spiral Diffusion with LoRA for RGB-to-RAW Conversion Across Cameras
- 基于信号依赖噪声权重的扩散框架，按像素亮度自适应重建质量。
- 在四个数据集上显著优于现有方法，提升下游检测精度。
- 引入轻量级相机适配模块，统一模型可快速适配不同相机特征。
与RGB相比，RAW图像能保留更高保真度和丰富的场景信息，对复杂成像条件下的任务至关重要。为降低数据采集成本，近期研究尝试从RGB合成RAW图像，但忽略了两个关键挑战：(i) 重建难度随像素亮度变化，(ii) 多相机转换需针对不同相机特性进行适配。为此，我们提出SpiralDiff，一种专用于RGB-to-RAW转换的扩散框架，采用信号依赖的噪声加权策略，实现不同亮度水平下的自适应重建保真度。此外，我们引入CamLoRA，一种相机感知的轻量级适配模块，使统一模型可快速适应不同相机的ISP特性。在四个基准数据集上的大量实验表明，SpiralDiff在RGB-to-RAW转换质量上具有显著优势，并在基于RAW的物体检测任务中带来下游性能提升。代码与模型已开源：https://github.com/Chuancy-TJU/SpiralDiff。
原文摘要 · Abstract (English)
RAW images preserve superior fidelity and rich scene information compared to RGB, making them essential for tasks in challenging imaging conditions. To alleviate the high cost of data collection, recent RGB-to-RAW conversion methods aim to synthesize RAW images from RGB. However, they overlook two key challenges: (i) the reconstruction difficulty varies with pixel intensity, and (ii) multi-camera conversion requires camera-specific adaptation. To address these issues, we propose SpiralDiff, a diffusion-based framework tailored for RGB-to-RAW conversion with a signal-dependent noise weighting strategy that adapts reconstruction fidelity across intensity levels. In addition, we introduce CamLoRA, a camera-aware lightweight adaptation module that enables a unified model to adapt to different camera-specific ISP characteristics. Extensive experiments on four benchmark datasets demonstrate the superiority of SpiralDiff in RGB-to-RAW conversion quality and its downstream benefits in RAW-based object detection. Our code and model are available at https://github.com/Chuancy-TJU/SpiralDiff.
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