Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

Liang Xu, Junping Du and Zhenhong Zhang

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high‐pass subbands to obtain the denoised high‐pass coefficients. The high‐pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low‐pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects.

Date: 2014
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https://doi.org/10.1155/2014/652128

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:652128

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