Title :
A joint reconstruction algorithm for multi-view compressed imaging
Author :
Kan Chang ; Tuanfa Qin ; Wenbo Xu ; Aidong Men
Author_Institution :
Sch. of Comput. & Electron. Inf., Guangxi Univ., Nanning, China
Abstract :
As compressed sensing can capture signal at sub-Nyquist rate, it is suitable to apply multi-view compressed imaging framework in vision sensor networks. The image views in such networks are correlated with each other, and therefore the performance of independent view reconstruction can be further improved by joint reconstruction. In this paper, we propose a joint reconstruction algorithm, where disparity estimation and disparity compensation are used to exploit the correlation between views. The target optimization problem is divided into two sub-problems and they are solved alternately by proximal-gradient method. We show by experiments that, for a given sub-rate, the proposed joint reconstruction scheme outperforms the independent reconstruction in terms of image quality.
Keywords :
gradient methods; image reconstruction; image sensors; optimisation; compressed sensing; disparity compensation; disparity estimation; image quality; independent view reconstruction; joint reconstruction algorithm; multiview compressed imaging framework; proximal-gradient method; subNyquist rate; target optimization problem; vision sensor networks; Compressed sensing; Correlation; Image coding; Image reconstruction; Joints; PSNR; Reconstruction algorithms;
Conference_Titel :
Circuits and Systems (ISCAS), 2013 IEEE International Symposium on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-5760-9
DOI :
10.1109/ISCAS.2013.6571822