Multi-resolution convolutional neural networks for inverse problems

Inverse problems in image processing, phase imaging, and computer vision often share the same structure of mapping input image(s) to output image(s) but are usually solved by different application-specific algorithms. Deep convolutional neural networks have shown great potential for highly variable...

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Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; p. 5730
Main Authors: Wang, Feng, Eljarrat, Alberto, Müller, Johannes, Henninen, Trond R., Erni, Rolf, Koch, Christoph T.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 31.03.2020
Nature Publishing Group
Subjects:
639/301
639/624
639/766/930/12
639/766/930/2735
639/766/930/328
Computer vision
Humanities and Social Sciences
Image processing
Inverse problems
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Transmission electron microscopy
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Inverse problems in image processing, phase imaging, and computer vision often share the same structure of mapping input image(s) to output image(s) but are usually solved by different application-specific algorithms. Deep convolutional neural networks have shown great potential for highly variable tasks across many image-based domains, but sometimes can be challenging to train due to their internal non-linearity. We propose a novel, fast-converging neural network architecture capable of solving generic image(s)-to-image(s) inverse problems relevant to a diverse set of domains. We show this approach is useful in recovering wavefronts from direct intensity measurements, imaging objects from diffusely reflected images, and denoising scanning transmission electron microscopy images, just by using different training datasets. These successful applications demonstrate the proposed network to be an ideal candidate solving general inverse problems falling into the category of image(s)-to-image(s) translation.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-62484-z