Improved hybrid method for image super-resolution

被引：0

作者：

Xing, Weiwei ^{[1
]}

Zhao, Yahui ^{[1
]}

Bao, Ergude ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Software Engn Res Ctr, Sch Software Engn, Beijing 100044, Peoples R China

来源：

IET COMPUTER VISION | 2016年 / 10卷 / 08期

基金：

中国国家自然科学基金;

关键词：

SPARSE; ALGORITHM;

D O I：

10.1049/iet-cvi.2015.0464

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：769 / 779

页数：11

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