Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in Wavelet based contourlet transform domain

2010 ◽  
Author(s):  
Xiaofen Wang ◽  
Legeng Chen
Keyword(s):  
Neural Networks ◽  
Spatial Frequency ◽  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Fusion Algorithm ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

scholarly journals Image Fusion Based on Nonsubsampled Contourlet Transform and Saliency-Motivated Pulse Coupled Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Xu ◽  
Junping Du ◽  
Qingping Li
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Main Idea ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Coupled Neural Networks ◽  
Fused Image ◽  
Pulse Coupled Neural Networks ◽  
High Pass

In the nonsubsampled contourlet transform (NSCT) domain, a novel image fusion algorithm based on the visual attention model and pulse coupled neural networks (PCNNs) is proposed. For the fusion of high-pass subbands in NSCT domain, a saliency-motivated PCNN model is proposed. The main idea is that high-pass subband coefficients are combined with their visual saliency maps as input to motivate PCNN. Coefficients with large firing times are employed as the fused high-pass subband coefficients. Low-pass subband coefficients are merged to develop a weighted fusion rule based on firing times of PCNN. The fused image contains abundant detailed contents from source images and preserves effectively the saliency structure while enhancing the image contrast. The algorithm can preserve the completeness and the sharpness of object regions. The fused image is more natural and can satisfy the requirement of human visual system (HVS). Experiments demonstrate that the proposed algorithm yields better performance.

scholarly journals Multifocus Color Image Fusion Based on NSST and PCNN

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Jin ◽  
Rencan Nie ◽  
Dongming Zhou ◽  
Quan Wang ◽  
Kangjian He
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Color Image ◽  
Color Space ◽  
Color Spaces ◽  
Fusion Algorithm ◽  
Hsv Color Space ◽  
Coupled Neural Networks ◽  
Different Color ◽  
Pulse Coupled Neural Networks

This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed to get the RGB color image. The experimental results indicate that the proposed color image fusion algorithm is more efficient than other common color image fusion algorithms.

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