Image Quality Enhancement for Single-Image Super Resolution Based on Local Similarities and Support Vector Regression

Atsushi YAGUCHI  Tadaaki HOSAKA  Takayuki HAMAMOTO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E94-A   No.2   pp.552-554
Publication Date: 2011/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E94.A.552
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Image Media Quality)
Category: Processing
super resolution,  local similarity,  support vector regression (SVR),  total variation (TV),  

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In reconstruction-based super resolution, a high-resolution image is estimated using multiple low-resolution images with sub-pixel misalignments. Therefore, when only one low-resolution image is available, it is generally difficult to obtain a favorable image. This letter proposes a method for overcoming this difficulty for single- image super resolution. In our method, after interpolating pixel values at sub-pixel locations on a patch-by-patch basis by support vector regression, in which learning samples are collected within the given image based on local similarities, we solve the regularized reconstruction problem with a sufficient number of constraints. Evaluation experiments were performed for artificial and natural images, and the obtained high-resolution images indicate the high-frequency components favorably along with improved PSNRs.