On the use of joint sparse representation for image fusion quality evaluation and analysis

被引：9

作者：

Hu, Yanxiang ^{[1
]}

Gao, Qian ^{[1
]}

Zhang, Bo ^{[1
]}

Zhang, Juntong ^{[1
]}

机构：

[1] Tianjin Normal Univ, Coll Comp & Informat Engn, 393 Binshui West Rd, Tianjin 300387, Peoples R China

来源：

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | 2019年 / 61卷

基金：

美国国家科学基金会;

关键词：

Image fusion; Quality evaluation; Sparse representation; joint sparse representation; Atom remnant analysis; MULTI-FOCUS; PERFORMANCE; TRANSFORM; RECOVERY;

D O I：

10.1016/j.jvcir.2019.04.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a Spare Representation (SR) based fusion quality evaluation and analysis method is proposed. This method employs Joint Sparse Representation (JSR) to extract the source image remnants after fusion. These atom-level remnants indicate the fusion quality intuitively, and permit the analysis of fusion effect in learned feature space. Our analysis results indicate that high salient atoms always present poor expressions in fusion results. An improved fusion rule is designed to emphasis high salient atoms accordingly. In experiments, the effectiveness of our method was verified and the characteristics of atom JSR remnants were investigated in detail first. Then the new fusion rule was tested to demonstrate the value of JSR remnant analysis. The objective and subjective comparison results indicate that the proposed analytical evaluation metric can measure fusion quality and analysis atom fusion effect accurately. The new fusion rule provides a valuable alternative for SR fusion algorithm design. (C) 2019 Elsevier Inc. All rights reserved.

引用

页码：225 / 235

页数：11