  Imaging under extremely low-light conditions presents a significant challenge
and is an ill-posed problem due to the low signal-to-noise ratio (SNR) caused
by minimal photon capture. Previously, diffusion models have been used for
multiple kinds of generative tasks and image-to-image tasks, however, these
models work as a post-processing step. These diffusion models are trained on
processed images and learn on processed images. However, such approaches are
often not well-suited for extremely low-light tasks. Unlike the task of
low-light image enhancement or image-to-image enhancement, we tackle the task
of learning the entire image-processing pipeline, from the RAW image to a
processed image. For this task, a traditional image processing pipeline often
consists of multiple specialized parts that are overly reliant on the
downstream tasks. Unlike these, we develop a new generative ISP that relies on
fine-tuning latent diffusion models on RAW images and generating processed
long-exposure images which allows for the apt use of the priors from large
text-to-image generation models. We evaluate our approach on popular end-to-end
low-light datasets for which we see promising results and set a new SoTA on the
See-in-Dark (SID) dataset. Furthermore, with this work, we hope to pave the way
for more generative and diffusion-based image processing and other problems on
RAW data.
