Large image reconstruction based on sparse-banded matrix

Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (10): 184-187.

Previous Articles Next Articles

Large image reconstruction based on sparse-banded matrix

YANG Hairong1, FANG Hong2, ZHANG Cheng3, PAN Gen’an1

1.Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China
2.School of Science, Shanghai Second Polytechnic University, Shanghai 201209, China
3.Department of Mathematics, Hefei Normal University, Hefei 230039, China

Online:2013-05-15 Published:2013-05-14

基于稀疏带状矩阵的二维图像重建

杨海蓉1，方红2，张成3，潘根安1

1.安徽大学计算智能与信号处理教育部重点实验室，合肥 230039
2.上海第二工业学院理学院，上海 201209
3.合肥师范学院数学系，合肥 230069

Abstract

Abstract: Compressed Sensing（CS） is a new technique for simultaneous data sampling and compression. In this paper, aiming at the dimension of measurement matrix is too high for large images to storage, sparse-banded matrix is introduced for reconstructing signals. It reduces independent random variables of measurement matrix. Through processing the large images column by column, the dimension of measurement matrix is greatly reduced. The experimental results show that the image reconstruction algorithm, based on sparse-banded measurement matrix and the approach of column by column, ensures reconstruction quality of images and lower the reconstruction speed.

Key words: compressive sensing, circulant matrix, sparse banded matrix

摘要： 压缩传感（Compressed Sensing，CS）是数据采样同时实现压缩的新理论、新技术。针对大图像重构时采用的测量矩阵维数高，所需存储空间过大的问题，引入稀疏带状概念，提出了稀疏带状测量矩阵，可减少测量矩阵独立随机元，根据图像按列逐步处理的方式，测量矩阵维数大大降低。实验结果表明基于稀疏带状测量矩阵的逐列图像重构算法在保证重建质量的情况下，计算速度也大大提升。

关键词: 压缩传感, 循环矩阵, 稀疏带状矩阵

YANG Hairong1, FANG Hong2, ZHANG Cheng3, PAN Gen’an1. Large image reconstruction based on sparse-banded matrix[J]. Computer Engineering and Applications, 2013, 49(10): 184-187.

杨海蓉1，方红2，张成3，潘根安1. 基于稀疏带状矩阵的二维图像重建[J]. 计算机工程与应用, 2013, 49(10): 184-187.

[1]	ZHANG Shanshan, ZHAO Jianhua. Through-the-Wall Radar Imaging Algorithm Based on Inexact Augmented Lagrange Multiplier [J]. Computer Engineering and Applications, 2021, 57(11): 77-83.
[2]	ZHU Ruijin, GONG Xuejiao, TANG Bo. Distributed Hybrid Compressive Sensing for Wireless Sensor Network Data Collection [J]. Computer Engineering and Applications, 2019, 55(6): 73-80.
[3]	JI Xiaohui, SUN Zeyu, YAN Ben, LI Chuanfeng. High-Order MIMO Systems Channel Estimation Algorithms Based on Compressive Sensing [J]. Computer Engineering and Applications, 2019, 55(19): 153-159.
[4]	YANG Hairong1, JIN Hui2. Improved IRLS algorithm for nonconvex compressive sensing [J]. Computer Engineering and Applications, 2018, 54(24): 46-51.
[5]	LI Kangda, LIU Hao, WANG Bing, SUN Xiaofan, ZHANG Xinsheng. Compressive sampling allocation algorithm based on hybrid quality of image set [J]. Computer Engineering and Applications, 2018, 54(22): 191-196.
[6]	CHENG Lingfei, GUAN Haichao, WANG Keping. Improved compressive tracking algorithm [J]. Computer Engineering and Applications, 2017, 53(6): 183-187.
[7]	LV Weijie, CHEN Xia, LIU Hongzhen. Image adaptive subspace pursuit algorithm based on compressive sensing [J]. Computer Engineering and Applications, 2016, 52(3): 220-223.
[8]	YAN Quanfeng1，2, WANG Yuebin1, BAI Tian1, SHEN Yanfei3. Real-time gesture detection and tracking algorithm based on compressive sensing [J]. Computer Engineering and Applications, 2016, 52(20): 182-187.
[9]	CAO Yiqin1, ZHOU Xiaoci1, HUANG Xiaosheng2. Target tracking algorithm based on complementary feature weighting of compressive sensing [J]. Computer Engineering and Applications, 2016, 52(19): 213-217.
[10]	TANG Yu1, LING Zhigang1, LI Jiancheng2, BAI Lu2. Compressive tracking based on adaptive feature fusion [J]. Computer Engineering and Applications, 2015, 51(10): 160-165.
[11]	GUO Peng. Adaptive measurement algorithm based on Bayesian Compressive Sensing [J]. Computer Engineering and Applications, 2013, 49(9): 200-202.
[12]	CHANG Qingwen1, SHEN Xiaohong1, CHANG Juan2. Study on application of channel estimation algorithms in OFDM underwater acoustic communication [J]. Computer Engineering and Applications, 2013, 49(8): 85-87.
[13]	SONG Caifeng, LIU Weifeng, WANG Yanjiang. Mixture facial expression analysis using sparse representation [J]. Computer Engineering and Applications, 2013, 49(23): 122-126.
[14]	XIANG Jin, LI Cuihong, WEI Deyi, YANG Jiahong. Fast reconstruction for compressive sensing image using adjacency matrix of bipartite graph [J]. Computer Engineering and Applications, 2013, 49(1): 191-194.
[15]	TANG Yajuan, LI Qing. Application of compressive sensing in spectrum sensing of digital television signal [J]. Computer Engineering and Applications, 2012, 48(3): 126-128.

Large image reconstruction based on sparse-banded matrix

基于稀疏带状矩阵的二维图像重建

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics