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
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