RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation
This enables efficient creation of RAW datasets for computer vision tasks like object detection, particularly in challenging conditions such as low-light environments, though it is incremental as it adapts diffusion models to a specific domain.
The paper tackles the problem of generating high-fidelity RAW images from RGB inputs to address the scarcity of camera-specific RAW datasets, achieving state-of-the-art performance on four DSLR datasets and demonstrating exceptional data efficiency with as few as 25 training samples.
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.