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Unsupervised Image Restoration Using Partially Linear Denoisers.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Ke, Rihuan 
Schonlieb, Carola-Bibiane 

Abstract

Deep neural network based methods are the state of the art in various image restoration problems. Standard supervised learning frameworks require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth, clean images is minimized. The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications. We circumvent this problem by proposing a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a linear noise-dependent term and a small residual term. We show that these denoisers can be trained with only noisy images under the condition that the noise has zero mean and known variance. The exact distribution of the noise, however, is not assumed to be known. We show the superiority of our approach for image denoising, and demonstrate its extension to solving other restoration problems such as image deblurring where the ground truth is not available. Our method outperforms some recent unsupervised and self-supervised deep denoising models that do not require clean images for their training. For deblurring problems, the method, using only one noisy and blurry observation per image, reaches a quality not far away from its fully supervised counterparts on a benchmark dataset.

Description

Keywords

4611 Machine Learning, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, Machine Learning and Artificial Intelligence

Journal Title

IEEE Trans Pattern Anal Mach Intell

Conference Name

Journal ISSN

0162-8828
1939-3539

Volume Title

PP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Rights

All rights reserved
Sponsorship
Engineering and Physical Sciences Research Council (EP/N014588/1)
EPSRC (EP/S026045/1)
EPSRC (EP/T003553/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Leverhulme Trust (RPG-2018-121)
Leverhulme Trust (PLP-2017-275)
Alan Turing Institute (Unknown)
EPSRC (EP/T017961/1)
Royal Society (RSWF\R3\193016)