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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 536215, 9 pages
Research Article

PCNN-Based Image Fusion in Compressed Domain

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received 8 October 2014; Revised 6 January 2015; Accepted 7 January 2015

Academic Editor: Yi-Kuei Lin

Copyright © 2015 Yang Chen and Zheng Qin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.