[IPOL announce] new article: Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images
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Fri Dec 30 20:39:00 CET 2022
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.
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