Fast Iterative Enhancement for Image Signal Processing

The Image Signal Processor (ISP) is a key component in modern cameras that transforms the RAW scene radiance captured by the camera sensor into sRGB images that are suitable for the human visual system. The ISP is usually a model-based pipeline, and it comprises several stages (or blocks) such as denoising, white balance, color correction, and tone mapping. Many of these blocks are non-linear operations, or even deep neural networks. In this work, we propose the use of iterative diffusion models as an additional photo-finishing block in the imaging pipeline to produce high-quality sRGB images. This showcases the power of generative learned ISPs.