Multisource Image Fusion Based on DWT and Simplified Pulse Coupled Neural Network

2013 ◽  
Vol 457-458 ◽  
pp. 736-740 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Global Coupling ◽  
Maximum Selection ◽  
Pulse Synchronization

In this paper, a new image fusion algorithm based on discrete wavelet transform (DWT) and spiking cortical model (SCM) is proposed. The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network. Experimental results demonstrate the effectiveness of the proposed fusion method.

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.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

2018 ◽  
Vol 7 (2.31) ◽  
pp. 165
Author(s):  
M Shyamala Devi ◽  
P Balamurugan
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Gabor Filter ◽  
Low Frequency ◽  
Local Energy ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Image Averaging

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

An Image Fusion Algorithm Based on Redundant Wavelet Transform

2014 ◽  
Vol 687-691 ◽  
pp. 3656-3661
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Jin Yao Yang ◽  
Li Sha Sun
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Imaging Characteristics ◽  
Focus Image ◽  
Redundant Wavelet Transform

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

AN IMAGE FUSION FRAMEWORK BASED ON HUMAN VISUAL SYSTEM IN FRAMELET DOMAIN

2012 ◽  
Vol 10 (01) ◽  
pp. 1250002 ◽  
Author(s):  
GAURAV BHATNAGAR ◽  
Q. M. JONATHAN WU
Keyword(s):  
Visual System ◽  
Image Fusion ◽  
Human Visual System ◽  
High Frequency ◽  
Evaluation Criteria ◽  
Low Frequency ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Fusion Algorithm ◽  
Frequency Bands

In this paper, a novel image fusion algorithm based on framelet transform is presented. The core idea is to decompose all the images to be fused into low and high-frequency bands using framelet transform. For fusion, two different selection strategies are developed and used for low and high-frequency bands. The first strategy is adaptive weighted average based on local energy and is applied to fuse the low-frequency bands. In order to fuse high-frequency bands, a new strategy is developed based on texture while exploiting the human visual system characteristics, which can preserve more details in source images and further improve the quality of fused image. Experimental results demonstrate the efficiency and better performance than existing image fusion methods both in visual inspection and objective evaluation criteria.

Fusion Algorithm of Infrared and Visible Images Based on Multiresolution Decomposition

2012 ◽  
Vol 239-240 ◽  
pp. 229-232
Author(s):  
Chen Ding
Keyword(s):  
Statistical Model ◽  
Image Fusion ◽  
Frequency Band ◽  
High Frequency ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
High Frequency Band ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Multiresolution Decomposition

Information redundancy and complementarity are existing between the images obtained by multi-sensor, image fusion can improve the certainty and reliability of the information. Traditional method of image fusion based on multiresolution decomposition is susceptible to high frequency noise, fusion is often ineffective. A image fusion algorithm has been studied based on the wavelet multiresolution decomposition which is regional energy maximum for low-frequency decomposition image, and the bivariate statistical model for high-frequency part. The results show that: in the conditions of Daubechies 3 wavelet basis function, decomposition level 5 multiresolution decomposition, the bivariate statistical model for the high-frequency band is robust to noise based on the joint probability of wavelet coefficient pair - a wavelet coefficient and its parent; in the same time, the regional energy maximum for low-frequency band can be effective on the high-frequency band based on the bivariate statistical model. The fusion image has the biggish contrast, the preferable details, the higher gray level resolution.

scholarly journals Image Fusion Algorithm at Pixel Level Based on Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiming Chen ◽  
Liping Chen ◽  
Mohammad Shabaz
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Region ◽  
Experimental Comparison ◽  
Fusion Algorithm ◽  
High Frequency Region ◽  
Running Time ◽  
Low Efficiency

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.

scholarly journals Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jingming Xia ◽  
Yiming Chen ◽  
Aiyue Chen ◽  
Yicai Chen
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Information Transfer ◽  
Color Image ◽  
Low Frequency ◽  
Source Image ◽  
Medical Image Fusion

The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.

Multifocus Image Fusion Using Laplacian Pyramid and Gabor Filters

2013 ◽  
Vol 373-375 ◽  
pp. 530-535 ◽  
Author(s):  
Chuan Zhu Liao ◽  
Yu Shu Liu ◽  
Ming Yan Jiang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Gabor Filters ◽  
Experimental Results ◽  
Fusion Process ◽  
Laplacian Pyramid ◽  
Fusion Algorithm ◽  
Fusion Rules

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 new multifocus image fusion algorithm is proposed. The algorithm is based on Laplacian pyramid and Gabor filters. The source images are decomposed by Laplacian pyramid, then the directional edges feature and detail information can be obtained by Gabor filters. Different fusion rules are applied to the low frequency and high frequency coefficients. The experimental results show that the algorithm is simple and effective.

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