Non-convex optimization for robust multi-view imaging

We study the multi-view imaging problem where one has to reconstruct a set of l images, representing a single scene, from a few measurements made at different viewpoints. We first express the solution of the problem as the minimizer of a non-convex objective function where one needs to estimate one reference image, l foreground images modeling possible occlusions, and a set of l transformation parameters modeling the inter-correlation between the observations. Then, we propose an alternating descent method that attempts to minimize this objective function and produces a sequence converging to one of its critical points. Finally, experiments show that the method accurately recovers the original images and is robust to occlusions.


Presented at:
International Biomedical and Astronomical Signal Processing (BASP) Frontiers workshop, Villars-sur-Ollon, Switzerland, January, 2013
Year:
2013
Keywords:
Laboratories:




 Record created 2012-10-26, last modified 2018-12-02

Preprint:
Download fulltextPDF
External link:
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)