Fusion of infrared and visual images based on non-sampled Contourlet transform and region classification

Lei, 张蕾 Zhang; Long-xu, 金龙旭 Jin; Shuang-li, 韩双丽 Han; Zengming, 吕增明 Lv; Xin-e, 李新娥 Li

doi:10.3788/ope.20152303.0810

Cited by 12 publications

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“…Therefore, the Gibbs effect is come into being, bringing about the distortion of the fusion results. In 2006, Non-Subsampled Contourlet transform (NSCT) has been proposed on the basis of the Contourlet transform, which has not only enhanced the analysis of Contourlet in multi-directivity, but also overcame the distortion of fusion that is generated from the Gibbs effect, and thus, a high recognition has been gained [5,6].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Infrared and Visible Images based on NSCT and Modified PCNN

Zhou¹,

Gong²,

Xing³

2018

dtcse

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For purpose of improving the quality of multi-source fusion images, this paper introduces a fusion method of infrared and visible images based on the combination of the non-subsampled contourlet transform (NSCT) and the improved pulse coupled neural network model (PCNN). To begin with, registered images are decomposed by NSCT, the low frequency and high frequency sub-bands are also obtained. Then, the energy of Laplacian (SML) is used for the link intensity of PCNN, computing spatial frequency (SF) with a low frequency sub-band, and the gradient energy (EOG) with a high frequency sub-band. By means of treating them as external input of PCNN. Then fused coefficients are selected by sum of ignition output amplitude maximum, a fused image is finally obtained by inverse NSCT. Experimental results show that the fusion image has a high definition and effectively preserves information of the source image.

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Section: Introductionmentioning

confidence: 99%