DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913247563612160 |
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| author | Dagli, Rishit |
| author_facet | Dagli, Rishit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images Dagli, Rishit Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.18575 |