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Adaptive Compressive Sensing of Images Using Spatial Entropy

Compressive Sensing (CS) realizes a low-complex image encoding architecture, which is suitable for resource-constrained wireless sensor networks. However, due to the nonstationary statistics of images, images reconstructed by the CS-based codec have many blocking artifacts and blurs. To overcome the...

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Detalles Bibliográficos
Autores principales: Li, Ran, Duan, Xiaomeng, Guo, Xiaoli, He, Wei, Lv, Yongfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5672129/
https://www.ncbi.nlm.nih.gov/pubmed/29201042
http://dx.doi.org/10.1155/2017/9059204
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author Li, Ran
Duan, Xiaomeng
Guo, Xiaoli
He, Wei
Lv, Yongfeng
author_facet Li, Ran
Duan, Xiaomeng
Guo, Xiaoli
He, Wei
Lv, Yongfeng
author_sort Li, Ran
collection PubMed
description Compressive Sensing (CS) realizes a low-complex image encoding architecture, which is suitable for resource-constrained wireless sensor networks. However, due to the nonstationary statistics of images, images reconstructed by the CS-based codec have many blocking artifacts and blurs. To overcome these negative effects, we propose an Adaptive Block Compressive Sensing (ABCS) system based on spatial entropy. Spatial entropy measures the amount of information, which is used to allocate measuring resources to various regions. The scheme takes spatial entropy into consideration because rich information means more edges and textures. To reduce the computational complexity of decoding, a linear mode is used to reconstruct each block by the matrix-vector product. Experimental results show that our ABCS coding system provides a better reconstruction quality from both subjective and objective points of view, and it also has a low decoding complexity.
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spelling pubmed-56721292017-12-03 Adaptive Compressive Sensing of Images Using Spatial Entropy Li, Ran Duan, Xiaomeng Guo, Xiaoli He, Wei Lv, Yongfeng Comput Intell Neurosci Research Article Compressive Sensing (CS) realizes a low-complex image encoding architecture, which is suitable for resource-constrained wireless sensor networks. However, due to the nonstationary statistics of images, images reconstructed by the CS-based codec have many blocking artifacts and blurs. To overcome these negative effects, we propose an Adaptive Block Compressive Sensing (ABCS) system based on spatial entropy. Spatial entropy measures the amount of information, which is used to allocate measuring resources to various regions. The scheme takes spatial entropy into consideration because rich information means more edges and textures. To reduce the computational complexity of decoding, a linear mode is used to reconstruct each block by the matrix-vector product. Experimental results show that our ABCS coding system provides a better reconstruction quality from both subjective and objective points of view, and it also has a low decoding complexity. Hindawi 2017 2017-10-22 /pmc/articles/PMC5672129/ /pubmed/29201042 http://dx.doi.org/10.1155/2017/9059204 Text en Copyright © 2017 Ran Li et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Ran
Duan, Xiaomeng
Guo, Xiaoli
He, Wei
Lv, Yongfeng
Adaptive Compressive Sensing of Images Using Spatial Entropy
title Adaptive Compressive Sensing of Images Using Spatial Entropy
title_full Adaptive Compressive Sensing of Images Using Spatial Entropy
title_fullStr Adaptive Compressive Sensing of Images Using Spatial Entropy
title_full_unstemmed Adaptive Compressive Sensing of Images Using Spatial Entropy
title_short Adaptive Compressive Sensing of Images Using Spatial Entropy
title_sort adaptive compressive sensing of images using spatial entropy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5672129/
https://www.ncbi.nlm.nih.gov/pubmed/29201042
http://dx.doi.org/10.1155/2017/9059204
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