Image Fusion Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Networks

2011 ◽  
Author(s):  
Liu Fu ◽  
Liao Yifan ◽  
Liang Xin
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
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.

Image Fusion for Video Surveillance in Curvelet Domain

2013 ◽  
Vol 444-445 ◽  
pp. 1620-1624
Author(s):  
Xi Cai ◽  
Guang Han ◽  
Jin Kuan Wang
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Biological Activities ◽  
Fusion Method ◽  
Visual Effects ◽  
Global Features ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks ◽  
External Stimuli ◽  
Image Fusion Method

To simulate biological activities of human visual system, we propose a curvelet-based image fusion method using unit-linking pulse coupled neural networks (ULPCNNs) model. Contrasts of detailed coefficients are inputted into the ULPCNNs to imitate the sensitivity of HVS to detailed information, and the contrasts are also employed as corresponding linking strength for the neurons. After motivated by external stimuli from images, ULPCNNs can produce series of binary pulses containing much information of global features. Then we use the average firing times of output pulses in a neighborhood as the salience measure to determine our fusion rules. Experimental results demonstrate that, our proposed method has a satisfying fusion result both on visual effects and objective evaluations.

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