PCNN-Based Image Fusion in Compressed Domain
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper addresses a novel method of image fusion problem for different application scenarios, employing compressive sensing (CS) as the image sparse representation method and pulse-coupled neural network (PCNN) as the fusion rule.
Firstly, source images are compressed through scrambled block Hadamard ensemble (SBHE) for its compression capability and computational simplicity on the sensor side.
Local standard variance is input to motivate PCNN and coefficients with large firing times are selected as the fusion coefficients in compressed domain.
Fusion coefficients are smoothed by sliding window in order to avoid blocking effect.
Experimental results demonstrate that the proposed fusion method outperforms other fusion methods in compressed domain and is effective and adaptive in different image fusion applications.
American Psychological Association (APA)
Chen, Yang& Qin, Zheng. 2015. PCNN-Based Image Fusion in Compressed Domain. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074075
Modern Language Association (MLA)
Chen, Yang& Qin, Zheng. PCNN-Based Image Fusion in Compressed Domain. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1074075
American Medical Association (AMA)
Chen, Yang& Qin, Zheng. PCNN-Based Image Fusion in Compressed Domain. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074075
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074075