DocumentCode :
2958454
Title :
Dense disparity maps from sparse disparity measurements
Author :
Hawe, Simon ; Kleinsteuber, Martin ; Diepold, Klaus
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Tech. Univ. Munchen, München, Germany
fYear :
2011
fDate :
6-13 Nov. 2011
Firstpage :
2126
Lastpage :
2133
Abstract :
In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.
Keywords :
conjugate gradient methods; image reconstruction; optimisation; compressive sensing theory; conjugate subgradient method; dense disparity map estimation; disparity map reconstruction; optimization problem; sparse disparity measurements; Coherence; Compressed sensing; Equations; Image reconstruction; Vectors; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2011 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1550-5499
Print_ISBN :
978-1-4577-1101-5
Type :
conf
DOI :
10.1109/ICCV.2011.6126488
Filename :
6126488
Link To Document :
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