TY - GEN
T1 - Compressed sensing recovery of multi-view video sequences by targeted database
AU - You, Yong
AU - Liu, Bin
AU - Chen, Chang Wen
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/18
Y1 - 2014/12/18
N2 - Compressed sensing (CS) is a technique that enables signal reconstruction at sub-Nyquist rate and has been widely used for fast reconstruction of multi-view video sequences (MVS) in the surveillance application. In this paper, we propose compressed-sensing recovery of MVS exploiting the model of structural group sparse representation (SGSR) along with a targeted database. SGSR groups similar patches together coupled with learning the adaptive basis from the similar groups, which gets sparser representation and thereby performs better in CS recovery. A targeted database can be easily obtained from the MVS due to their abundant prior information and the database can help us obtain more accurate similar patches, which further improve the performance with SGSR. Considering images as compressible signals rather than sparse signals, we design a filtering to retain the details of images. Simulation results show that the proposed algorithm outperforms existing reconstruction algorithms.
AB - Compressed sensing (CS) is a technique that enables signal reconstruction at sub-Nyquist rate and has been widely used for fast reconstruction of multi-view video sequences (MVS) in the surveillance application. In this paper, we propose compressed-sensing recovery of MVS exploiting the model of structural group sparse representation (SGSR) along with a targeted database. SGSR groups similar patches together coupled with learning the adaptive basis from the similar groups, which gets sparser representation and thereby performs better in CS recovery. A targeted database can be easily obtained from the MVS due to their abundant prior information and the database can help us obtain more accurate similar patches, which further improve the performance with SGSR. Considering images as compressible signals rather than sparse signals, we design a filtering to retain the details of images. Simulation results show that the proposed algorithm outperforms existing reconstruction algorithms.
UR - https://www.scopus.com/pages/publications/84921665866
U2 - 10.1109/WCSP.2014.6992202
DO - 10.1109/WCSP.2014.6992202
M3 - Conference contribution
AN - SCOPUS:84921665866
T3 - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
BT - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
Y2 - 23 October 2014 through 25 October 2014
ER -