Block compressive sensing method based on adaptive sampling and smooth projection

被引：0

作者：

Shi C. ^{[1
,2
]}

Wang L. ^{[1
]}

Na Y. ^{[2
]}

Huang B. ^{[3
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] College of Communication and Electronic Engineering, Qiqihar University, Qiqihar

[3] China Telecom Co. Hechi Branch, Hechi

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2020年 / 41卷 / 06期

关键词：

Adaptive sampling; Compressive sensing; Filter; Multi-scale block; Multi-scale reconstruction; Reconstruction; Smooth projection; Sparse performance;

D O I：

10.11990/jheu.201901115

中图分类号：

学科分类号：

摘要：

In the traditional compressive sensing algorithm based on a multi-scale wavelet transform, the fixed bit rate is allocated to each subband, which limits the quality of reconstructed images. In this paper, a block compressive sensing method based on adaptive sampling and smooth projection is proposed. This method reconstructs images in the spatial and sparse domains of images. On the basis of each segmentation block, it uses a smoothing filter for smoothing projection in the spatial domain and makes sparse transformation and threshold processing in the sparse domain. Moreover, different observation matrices are used to observe the wavelet coefficients of each layer, thereby improving the block effect. Through adaptive sampling, the method overcomes the problem where the sparse performance is restricted by the same sampling rate among subblocks. The experimental results show that through the proposed block compressive sensing algorithm, we can obtain better reconstructed images at different sampling rates, and the reconstructed images are faster than those from the traditional method. © 2020, Editorial Department of Journal of HEU. All right reserved.

引用

页码：877 / 883

页数：6

共 17 条

[1] CANDES E J, ROMBERG J, TAO T., Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J], IEEE transactions on information theory, 52, 2, pp. 489-509, (2006)
[2] CANDES E J, TAO T., Near-optimal signal recovery from random projections:universal encoding strategies?[J], IEEE transactions on information theory, 52, 12, pp. 5406-5425, (2006)
[3] SHEN Yanfei, ZHU Zhenmin, ZHANG Yongdong, Et al., Compressed sensing image reconstruction algorithm based on rank minimization, ACTA electronica sinica, 44, 3, pp. 572-579, (2016)
[4] LIU Jing, LI Xiaochao, ZHU Kaijian, Et al., Distributed compressed sensing based remote sensing image fusion algorithm, Journal of electronics & information technology, 39, 10, pp. 2374-2381, (2017)
[5] JIANG Yuan, MIAO Shengwei, LUO Huazhu, Et al., Improved search algorithm for compressive sensing image recovery based on Lp norm, Journal of image and graphics, 22, 4, pp. 435-442, (2017)
[6] ZHAO Chunhui, XU Yunlong, HUANG Hui, Et al., Sparse localization on the basis of Schmidt orthonormalization in wireless sensor networks, Journal of Harbin Engineering University, 35, 6, pp. 747-752, (2014)
[7] ESLAHI N, AGHAGOLZADEH A., Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization[J], IEEE transactions on image processing, 25, 7, pp. 3126-3140, (2016)
[8] MUSIC J, MARASOVIC T, PAPIC V, Et al., Performance of compressive sensing image reconstruction for search and rescue[J], IEEE geoscience and remote sensing letters, 13, 11, pp. 1739-1743, (2016)
[9] CHEN Zan, HOU Xingsong, QIAN Xueming, Et al., Efficient and robust image coding and transmission based on scrambled block compressive sensing[J], IEEE transactions on multimedia, 20, 7, pp. 1610-1621, (2018)
[10] GAN Lu, Block compressed sensing of natural images, Proceedings of the 15th International Conference on Digital Signal Processing, pp. 403-406, (2007)

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