Adaptive image fusion based on nonsubsampled contourlet transform

Zhang, Xiongmei; Li, Junshan; Yi, Zhaoxiang; Yang, Wei

doi:10.1117/12.749591

Cited by 3 publications

(2 citation statements)

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“…Fusion rule in high frequency subbands: maximum choosing. Figure 4(e) is the fusion result obtained by the method of literature [10]. …”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Nonsubsampled contourlet transform (NSCT) consists of the nonsubsampled pyramid (NSP) and nonsubsampled directional filter bank (NSDFB) [5] , which are independent, as shown in Figure 1. Firstly, the multi-scale decomposition is conducted by using NSP, and then different directions subbands coefficients are obtained by using NSDFB.…”

Section: Nonsubsampled Contourlet Transformmentioning

confidence: 99%

See 1 more Smart Citation

A Medical Image Fusion Method Based on NSCT and PCNN with Neighborhood Stimulation

Yang¹,

Wang²,

Li³

2018

dtetr

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In order to better guide clinical diagnosis and treatment, this paper proposes medical image fusion method based on NSCT (nonsubsampled contourlet transform) and PCNN (pulse coupled neural network) with neighborhood stimulation. A fusion rule based on PCNN with neighborhood standard deviation stimulation is adopted in low frequency sub-band coefficient. Through the application of the PCNN simplified model, the PCNN model is stimulated by neighborhood standard deviation according to human visual system. The low frequency sub-band coefficient is determined by firing times. When choosing the bandpass directional sub-band coefficients, a fusion rule based on region gradient energy is adopted according to directional characteristics of NSCT. The experiment results show that the proposed method provides abundant details and edge information over conventional methods in achieving the better quality of fusion.

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“…Fusion rule in high frequency subbands: maximum choosing. Figure 4(e) is the fusion result obtained by the method of literature [10]. …”

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Nonsubsampled Contourlet Transformmentioning

confidence: 99%