Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The resultant demand for comprehensive RAW image datasets contrasts with the labor-intensive process of creating specific datasets for individual sensors. To address this, we propose a novel diffusion-based method for generating RAW images guided by RGB images. Our approach integrates an RGB-guidance module for feature extraction from RGB inputs, then incorporates these features into the reverse diffusion process with RGB-guided residual blocks across various resolutions. This approach yields high-fidelity RAW images, enabling the creation of camera-specific RAW datasets. Our RGB2RAW experiments on four DSLR datasets demonstrate state-of-the-art performance. Moreover, RAW-Diffusion demonstrates exceptional data efficiency, achieving remarkable performance with as few as 25 training samples or even fewer. We extend our method to create BDD100K-RAW and Cityscapes-RAW datasets, revealing its effectiveness for object detection in RAW imagery, significantly reducing the amount of required RAW images.

翻译：当前计算机视觉领域的深度学习方法主要集中于RGB数据，这牺牲了部分信息。相比之下，RAW图像提供了更丰富的表征，这对于精确识别至关重要，尤其是在低光照等挑战性条件下。由此产生的对全面RAW图像数据集的需求，与为单个传感器创建特定数据集所需的劳动密集型过程形成了鲜明对比。为解决此问题，我们提出了一种新颖的、基于扩散的、由RGB图像引导的RAW图像生成方法。我们的方法集成了一个RGB引导模块，用于从RGB输入中提取特征，然后通过跨不同分辨率的RGB引导残差块将这些特征整合到反向扩散过程中。该方法能够生成高保真的RAW图像，从而能够创建针对特定相机的RAW数据集。我们在四个DSLR数据集上进行的RGB2RAW实验展示了最先进的性能。此外，RAW-Diffusion展现了卓越的数据效率，仅需25个甚至更少的训练样本即可实现出色的性能。我们扩展了该方法，创建了BDD100K-RAW和Cityscapes-RAW数据集，揭示了其在RAW图像目标检测中的有效性，并显著减少了所需的RAW图像数量。