Edge-Based Adaptive Sampling for Image Block Compressive Sensing

Lijing MA; Huihui BAI; Mengmeng ZHANG; Yao ZHAO

doi:10.1587/transfun.E99.A.2095

Edge-Based Adaptive Sampling for Image Block Compressive Sensing

Lijing MA, Huihui BAI, Mengmeng ZHANG, Yao ZHAO

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In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.11 pp.2095-2098

Publication Date: 2016/11/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E99.A.2095

Type of Manuscript: LETTER

Category: Image

Authors

Lijing MA
  Beijing Jiaotong University
Huihui BAI
  Beijing Jiaotong University
Mengmeng ZHANG
  North China University of Technology
Yao ZHAO
  Beijing Jiaotong University

Keyword

compressive sensing, adaptive sampling, iteration termination condition

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Lijing MA, Huihui BAI, Mengmeng ZHANG, Yao ZHAO, "Edge-Based Adaptive Sampling for Image Block Compressive Sensing" in IEICE TRANSACTIONS on Fundamentals, vol. E99-A, no. 11, pp. 2095-2098, November 2016, doi: 10.1587/transfun.E99.A.2095.
Abstract: In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.2095/_p

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@ARTICLE{e99-a_11_2095,
author={Lijing MA, Huihui BAI, Mengmeng ZHANG, Yao ZHAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Edge-Based Adaptive Sampling for Image Block Compressive Sensing},
year={2016},
volume={E99-A},
number={11},
pages={2095-2098},
abstract={In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.},
keywords={},
doi={10.1587/transfun.E99.A.2095},
ISSN={1745-1337},
month={November},}

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TY - JOUR
TI - Edge-Based Adaptive Sampling for Image Block Compressive Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2095
EP - 2098
AU - Lijing MA
AU - Huihui BAI
AU - Mengmeng ZHANG
AU - Yao ZHAO
PY - 2016
DO - 10.1587/transfun.E99.A.2095
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2016
AB - In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.
ER -