Open AccessImproved hybrid method for image super‐resolution
Author(s) -
Xing Weiwei,
Zhao Yahui,
Bao Ergude
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0464
Subject(s) - computer science , artificial intelligence , image (mathematics) , autoregressive model , computer vision , pattern recognition (psychology) , image restoration , frequency domain , image processing , mathematics , econometrics
Improving image resolution has broad applications and is an important research topic. Recently, a hybrid method Adaptive Sparse Domain Selection (ASDS) combining a reconstruction‐based method and an example‐based method has been proposed to take advantage of the two, but may not reconstruct sufficient details. In this study, the authors propose to improve ASDS: Zeyde's method is first used to obtain an intermediate image with high‐frequency details, and then the obtained image is used to replace the autoregressive model of ASDS as the example‐based term. In addition, the authors may split the input image into patches and use different parameter settings for the patches of different amount of details. Experimental results demonstrate the improved hybrid methods can produce high‐quality images quantitatively and perceptually.