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Noise-Adaptive Non-Blind Image Deblurring
This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical approaches to re...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503865/ https://www.ncbi.nlm.nih.gov/pubmed/36146269 http://dx.doi.org/10.3390/s22186923 |
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author | Slutsky, Michael |
author_facet | Slutsky, Michael |
author_sort | Slutsky, Michael |
collection | PubMed |
description | This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical approaches to regularized image deconvolution is provided, and common issues arising in this context are described. It is shown how a pre-deconvolved deep neural network (DNN) based image enhancement can be improved by joint optimization of regularization parameters and network weights. Furthermore, a two-step approach to deblurring based on two DNNs is proposed, with the first network estimating deconvolution regularization parameters, and the second one performing image enhancement and residual artifact removal. For the first network, a novel RegParamNet architecture is introduced and its performance is examined for both direct and indirect regularization parameter estimation. The system is shown to operate well for input noise in a three orders of magnitude range (0.01–10.0) and a wide spectrum of 1D or 2D Gaussian blur kernels, well outside the scope of most previously explored image blur and noise degrees. The proposed method is found to significantly outperform several leading state-of-the-art approaches. |
format | Online Article Text |
id | pubmed-9503865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95038652022-09-24 Noise-Adaptive Non-Blind Image Deblurring Slutsky, Michael Sensors (Basel) Article This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical approaches to regularized image deconvolution is provided, and common issues arising in this context are described. It is shown how a pre-deconvolved deep neural network (DNN) based image enhancement can be improved by joint optimization of regularization parameters and network weights. Furthermore, a two-step approach to deblurring based on two DNNs is proposed, with the first network estimating deconvolution regularization parameters, and the second one performing image enhancement and residual artifact removal. For the first network, a novel RegParamNet architecture is introduced and its performance is examined for both direct and indirect regularization parameter estimation. The system is shown to operate well for input noise in a three orders of magnitude range (0.01–10.0) and a wide spectrum of 1D or 2D Gaussian blur kernels, well outside the scope of most previously explored image blur and noise degrees. The proposed method is found to significantly outperform several leading state-of-the-art approaches. MDPI 2022-09-13 /pmc/articles/PMC9503865/ /pubmed/36146269 http://dx.doi.org/10.3390/s22186923 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Slutsky, Michael Noise-Adaptive Non-Blind Image Deblurring |
title | Noise-Adaptive Non-Blind Image Deblurring |
title_full | Noise-Adaptive Non-Blind Image Deblurring |
title_fullStr | Noise-Adaptive Non-Blind Image Deblurring |
title_full_unstemmed | Noise-Adaptive Non-Blind Image Deblurring |
title_short | Noise-Adaptive Non-Blind Image Deblurring |
title_sort | noise-adaptive non-blind image deblurring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503865/ https://www.ncbi.nlm.nih.gov/pubmed/36146269 http://dx.doi.org/10.3390/s22186923 |
work_keys_str_mv | AT slutskymichael noiseadaptivenonblindimagedeblurring |