Single Image Superresolution Based on Support Vector Regression

Author

Ni, Karl S. ; Kumar, Sanjeev ; Vasconcelos, Nuno ; Nguyen, Truong Q.

Author_Institution

Dept. of ECE, UCSD, La Jolla, CA

Volume

2

fYear

2006

fDate

14-19 May 2006

Abstract

Support vector machine (SVM) regression is considered for a statistical method of single frame superresolution in both the spatial and discrete cosine transform (DCT) domains. As opposed to current classification techniques, regression allows considerably more freedom in the determination of missing high-resolution information. In addition, since SVM regression approaches the superresolution problem as an estimation problem with a criterion of image correctness rather than visual acceptableness, its optimization results have better mean-squared error. With the addition of structure in the DCT coefficients, DCT domain image superresolution is further improved

Keywords

discrete cosine transforms; image resolution; mean square error methods; optimisation; regression analysis; support vector machines; discrete cosine transform; estimation problem; image correctness; mean-squared error; optimization results; single frame superresolution; single image superresolution; statistical method; support vector machine regression; Discrete cosine transforms; Error correction; Image resolution; Interpolation; Pixel; Spatial resolution; Statistical analysis; Statistical learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660414

Filename

1660414