DocumentCode :
3379836
Title :
Learning sparse image representation with support vector regression for single-image super-resolution
Author :
Yang, Ming-Chun ; Chu, Chao-Tsung ; Wang, Yu-Chiang Frank
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
1973
Lastpage :
1976
Abstract :
Learning-based approaches for super-resolution (SR) have been studied in the past few years. In this paper, a novel single-image SR framework based on the learning of sparse image representation with support vector regression (SVR) is presented. SVR is known to offer excellent generalization ability in predicting output class labels for input data. Given a low resolution image, we approach the SR problem as the estimation of pixel labels in its high resolution version. The feature considered in this work is the sparse representation of different types of image patches. Prior studies have shown that this feature is robust to noise and occlusions present in image data. Experimental results show that our method is quantitatively more effective than prior work using bicubic interpolation or SVR methods, and our computation time is significantly less than that of existing SVR-based methods due to the use of sparse image representations.
Keywords :
image representation; image resolution; interpolation; regression analysis; support vector machines; bicubic interpolation; image patches; learning based approach; low resolution image; pixel label estimation; single-image super-resolution; sparse image representation; support vector regression; Interpolation; Pixel; Spatial resolution; Strontium; Support vector machines; Training; Sparse Representation; Super-Resolution; Support Vector Regression (SVR);
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.2010.5654323
Filename :
5654323
Link To Document :
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