TY - GEN
T1 - An iterative weighing algorithm for image reconstruction in compressive sensing
AU - Hao, Chen
AU - Ma, Xiaoyang
AU - Ye, Zhang
AU - Chen, Hao
AU - Tang, Wenyan
PY - 2010
Y1 - 2010
N2 - Compressive sensing (CS), is a framework which points us a promising way of not measuring N-dimensional signals directly, but rather a set of related measurements, which a linear combination of the original underlying N-dimensional signal. However, the traditional CS reconstruction methods use l1-norm optimization which usually gives poor performance on 2D signal or only suit for specific natural image. In this paper, an iterative weighing algorithm for image reconstruction in CS is proposed. According to the sparsity of last iteration, the algorithm iteratively refines the weighting coefficients to enhance the sparsity of the reconstruction results until the convergence is reached. The experiments for natural image and remote sensing image demonstrate that the proposed method can outperforms the traditional CS framework in image reconstruction in the sense that the PSNR of reconstruction image improve over 2dB in the average.
AB - Compressive sensing (CS), is a framework which points us a promising way of not measuring N-dimensional signals directly, but rather a set of related measurements, which a linear combination of the original underlying N-dimensional signal. However, the traditional CS reconstruction methods use l1-norm optimization which usually gives poor performance on 2D signal or only suit for specific natural image. In this paper, an iterative weighing algorithm for image reconstruction in CS is proposed. According to the sparsity of last iteration, the algorithm iteratively refines the weighting coefficients to enhance the sparsity of the reconstruction results until the convergence is reached. The experiments for natural image and remote sensing image demonstrate that the proposed method can outperforms the traditional CS framework in image reconstruction in the sense that the PSNR of reconstruction image improve over 2dB in the average.
KW - 2D sparsity
KW - Compressive sensing(CS)
KW - Image reconstruction
KW - Iterative weighting algorithm
UR - https://www.scopus.com/pages/publications/78650493094
U2 - 10.1109/PCSPA.2010.268
DO - 10.1109/PCSPA.2010.268
M3 - 会议稿件
AN - SCOPUS:78650493094
SN - 9780769541808
T3 - Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
SP - 1091
EP - 1094
BT - Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
T2 - 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Y2 - 17 September 2010 through 19 September 2010
ER -