scholarly journals Multifocus Image Fusion Based on Multiresolution Transform and Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3281-3285 ◽  
Author(s):  
Yu Shu Liu ◽  
Ming Yan Jiang ◽  
Chuan Zhu Liao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Fusion ◽  
High Frequency ◽  
Particle Swarm ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Swarm Optimization ◽  
Fused Image ◽  
Multiresolution Transform

In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a novel multifocus image fusion algorithm based on multiresolution transform and particle swarm optimization (PSO) is proposed. Firstly the source images are decomposed into lowpass subbands coefficients and highpass subbands coefficients by the nonsubsampled contourlet transform (NSCT). Then, different fusion rules are applied for low and high frequency NSCT coefficients. Finally the fused image is reconstructed by the inverse NSCT transform. The experiment results demonstrate that the proposed method is effective and can provide better performance than the method based on the wavelet transform and the nonsubsampled contourlet transform.

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 401-403 ◽  
pp. 1381-1384 ◽  
Author(s):  
Zi Juan Luo ◽  
Shuai Ding
Keyword(s):  
Image Fusion ◽  
Multiple Scales ◽  
Weighted Average ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Limited Depth

t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

A New Image Fusion Method Based on Improved PCNN and Multiscale Decomposition

2013 ◽  
Vol 834-836 ◽  
pp. 1011-1015 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Maximum Selection

A new image fusion algorithm based on nonsubsampled contourlet transform and spiking cortical model is proposed in this paper. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands of nonsubsampled contourlet transform respectively. A new maximum selection rule is defined to fuse low frequency coefficients. Spatial frequency is used for the fusion rule of high frequency coefficients. Experimental results demonstrate the effectiveness of the proposed fusion method.

scholarly journals COMPRESSIVE SENSING IMAGE FUSION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 785-790
Author(s):  
X. Li ◽  
J. Lv ◽  
S. Jiang ◽  
H. Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Image Fusion ◽  
Compressive Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Fusion Algorithm ◽  
Swarm Optimization ◽  
Fusion Coefficient

In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.

IMAGE FUSION ALGORITHM BASED ON NONSUBSAMPLED CONTOURLET TRANSFORM AND ESTIMATION THEORY

2009 ◽  
Vol 06 (02) ◽  
pp. 109-116
Author(s):  
GAO-PENG ZHAO ◽  
YU-MING BO
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Estimation Theory ◽  
Contourlet Transform ◽  
Model Parameters ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fusion Procedure ◽  
Fused Image

Aimed at the fusion of infrared and visual images, and their application demands, a new image fusion method was proposed based on the nonsubsampled contourlet transform and estimation theory. Firstly, the nonsubsampled contourlet transform was employed to decompose the source images into the low frequency subband coefficient and bandpass directional subband coefficients. Then, for the bandpass directional subband coefficients, the detail coefficients were modeled by the Gaussian mixture distributions and the EM algorithm was used in conjunction with the model to develop an iterative fusion procedure to estimate the model parameters and to produce the fused coefficients; for the fusion of the approximate subband coefficients, the rule was employed based on the energy of the pixel neighboring region. Finally, the fused image was obtained by applying the inverse nonsubsampled contourlet transform. The experimental results showed that the fusion scheme is effective and the fused image is better than that of using the wavelet transform and the contourlet transform.

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 Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Kangjian He ◽  
Dongming Zhou ◽  
Xuejie Zhang ◽  
Rencan Nie
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Weighted Average ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Background Region ◽  
Visible Image ◽  
Region Detection ◽  
Fused Image

The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.

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