Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images
Valéry Dewil
⚠ This is a preprint. It may change before it is accepted for publication.


Access to high quality datasets is an essential condition for data-driven methods as it is known that mismatches between the distributions of training and test data may cause learning-based methods to fail. This issue has led to one of the most active research subjects in learning-based image restoration. For instance neural networks trained on unrealistic synthetic data may not generalize to real data even if they perform well on those synthetic data. This is specially problematic for image and video processing tasks, such as denoising, which are performed on raw data, since acquiring real raw datasets is not straightforward and is even impossible in some cases (acquiring a video dataset of real noise with clean ground-truth, for instance). Consequently, CNNs are often trained on synthetic data. Synthesizing realistic raw data is a difficult task and requires to invert properly the image processing pipeline. This paper focuses on the backward pipeline proposed by Brooks et al. [Unprocessing images for learned raw denoising, CVPR 2019] which aims at producing raw data from sRGB images.

This is an MLBriefs article, the source code has not been reviewed!