Disparity-compensated total-variation minimization for compressed-sensed multiview image reconstruction

Ying Liu; Chen Zhang; Joohee Kim

ICASSP 2015accepted0 citations

Disparity-compensated total-variation minimization for compressed-sensed multiview image reconstruction

Abstract

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, we consider a distributed multi-view imaging system where each camera at a different location performs independent compressed sensing acquisition of the target scene. At the decoder, we propose a disparity-compensated total-variation (TV) minimization algorithm to jointly reconstruct the multiple views. Experimental results show that the proposed joint decoding algorithm outperforms significantly independent-view decoding as well as disparity-compensated residue-view reconstruction algorithm.

BibTeX

@inproceedings{icassp2015_disparitycompens,
  title = {Disparity-compensated total-variation minimization for compressed-sensed multiview image reconstruction},
  author = {Ying Liu and Chen Zhang and Joohee Kim},
  booktitle = {ICASSP 2015},
  year = {2015}
}