pcnn-based image fusion in compressed domain

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ID: 223926
2015
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Abstract
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.
Reference Key
chen2015mathematicalpcnn-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yang Chen;Zheng Qin
Journal journal of power sources
Year 2015
DOI
10.1155/2015/536215
URL
http://dx.doi.org/10.1155/2015/536215
Keywords
civil engineering (general)mathematics engineering (general)

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