Cargando…
Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior
The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixe...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611294/ https://www.ncbi.nlm.nih.gov/pubmed/36298213 http://dx.doi.org/10.3390/s22207858 |
_version_ | 1784819490079375360 |
---|---|
author | Zhang, Ziyu Zheng, Liangliang Xu, Wei Gao, Tan Wu, Xiaobin Yang, Biao |
author_facet | Zhang, Ziyu Zheng, Liangliang Xu, Wei Gao, Tan Wu, Xiaobin Yang, Biao |
author_sort | Zhang, Ziyu |
collection | PubMed |
description | The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixels, such as the extreme channels. The features are extracted in units of patches, which are segmented from an image and partially overlap with each other. In this paper, we design a new prior, i.e., overlapped patches’ non-linear (OPNL) prior, derived from the ratio of extreme pixels affected by blurring in patches. The analysis of more than 5000 remote sensing images confirms that OPNL prior prefers clear images rather than blurry images in the restoration process. The complexity of the optimization problem is increased due to the introduction of OPNL prior, which makes it impossible to solve it directly. A related solving algorithm is established based on the projected alternating minimization (PAM) algorithm combined with the half-quadratic splitting method, the fast iterative shrinkage-thresholding algorithm (FISTA), fast Fourier transform (FFT), etc. Numerous experiments prove that this algorithm has excellent stability and effectiveness and has obtained competitive processing results in restoring remote sensing images. |
format | Online Article Text |
id | pubmed-9611294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96112942022-10-28 Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior Zhang, Ziyu Zheng, Liangliang Xu, Wei Gao, Tan Wu, Xiaobin Yang, Biao Sensors (Basel) Article The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixels, such as the extreme channels. The features are extracted in units of patches, which are segmented from an image and partially overlap with each other. In this paper, we design a new prior, i.e., overlapped patches’ non-linear (OPNL) prior, derived from the ratio of extreme pixels affected by blurring in patches. The analysis of more than 5000 remote sensing images confirms that OPNL prior prefers clear images rather than blurry images in the restoration process. The complexity of the optimization problem is increased due to the introduction of OPNL prior, which makes it impossible to solve it directly. A related solving algorithm is established based on the projected alternating minimization (PAM) algorithm combined with the half-quadratic splitting method, the fast iterative shrinkage-thresholding algorithm (FISTA), fast Fourier transform (FFT), etc. Numerous experiments prove that this algorithm has excellent stability and effectiveness and has obtained competitive processing results in restoring remote sensing images. MDPI 2022-10-16 /pmc/articles/PMC9611294/ /pubmed/36298213 http://dx.doi.org/10.3390/s22207858 Text en © 2022 by the authors. 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 Zhang, Ziyu Zheng, Liangliang Xu, Wei Gao, Tan Wu, Xiaobin Yang, Biao Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title | Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title_full | Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title_fullStr | Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title_full_unstemmed | Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title_short | Blind Remote Sensing Image Deblurring Based on Overlapped Patches’ Non-Linear Prior |
title_sort | blind remote sensing image deblurring based on overlapped patches’ non-linear prior |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611294/ https://www.ncbi.nlm.nih.gov/pubmed/36298213 http://dx.doi.org/10.3390/s22207858 |
work_keys_str_mv | AT zhangziyu blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior AT zhengliangliang blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior AT xuwei blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior AT gaotan blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior AT wuxiaobin blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior AT yangbiao blindremotesensingimagedeblurringbasedonoverlappedpatchesnonlinearprior |