DocumentCode :
3356758
Title :
Total subset variation prior
Author :
Kumar, Sanjeev ; Nguyen, Truong Q.
Author_Institution :
Video Process. Lab., UCSD, La Jolla, CA, USA
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
77
Lastpage :
80
Abstract :
We propose total subset variation (TSV), a convexity preserving generalization of the total variation (TV) prior, for higher order clique MRF. A proposed differentiable approximation of the TSV prior makes it amenable for use in large images (e.g. 1080p). A convex relaxation of sub-exponential distribution is proposed as a criterion to determine the parameters of the optimization problem resulting from the TSV prior. For the super-resolution application, experiments show reconstruction error improvement with respect to the TV and other methods.
Keywords :
convex programming; image denoising; image resolution; convex relaxation; differentiable approximation; higher order clique MRF; large images; optimization problem; reconstruction error improvement; sub-exponential distribution; super-resolution application; total subset variation; Approximation methods; Image reconstruction; Image resolution; Optimization; Pixel; TV; Through-silicon vias; MRF; Super-resolution; Total Subset Variation; Total Variation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1522-4880
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2010.5652889
Filename :
5652889
Link To Document :
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