[IPOL announce] new article: Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2022/438/

Valéry Dewil,
Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images,
Image Processing On Line, 12 (2022), pp. 652–661.
https://doi.org/10.5201/ipol.2022.438

Abstract
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.