Image Fusion Based on Shearlet and Multi-Decision

2014 ◽  
Vol 889-890 ◽  
pp. 1103-1106
Author(s):  
Xin Zheng ◽  
Ai Ping Cai
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Fusion ◽  
High Frequency ◽  
Decision Rules ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Traditional Image

Image Fusion is an important and useful subject in Image Processing and Computer Vision. The traditional image fusion algorithm could not provide satisfactory fusion results. Aiming to solving this problem, in this paper, we proposed an algorithm based on shearlet and multi-decision. First we discussed the application of the shearlet transform. Then we use difference decision rules for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.

An Image Fusion Method Based on Shearlet and Multi-Decision

2014 ◽  
Vol 530-531 ◽  
pp. 390-393
Author(s):  
Yong Wang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Focus Image ◽  
Image Detail ◽  
Image Fusion Method ◽  
Traditional Image

Image processing is the basis of computer vision. Aiming at some problems existed in the traditional image fusion algorithm, a novel algorithm based on shearlet and multi-decision is proposed. At first we discussed multi-focus image fusion and then we use Shearlet transform and multi-decision for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach retains image detail and more clarity.

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 .

Fusion of Polarization Image Based on Curvelet Transform

2014 ◽  
Vol 536-537 ◽  
pp. 111-114 ◽  
Author(s):  
De Xiang Zhang ◽  
Hong Hai Wang ◽  
Feng Xue
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Fusion Algorithm ◽  
Information Measurement ◽  
Multi Scale ◽  
Fused Image ◽  
Polarization Image ◽  
Multi Scales

Curvelet transform is the combination of the multi-scale analysis and multi-directional analysis transforms, which is more suitable for objects with curves. Applications of the curvelet transform have increased rapidly in the field of image fusion. Firstly, using the curvelet transform, several polarization images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused curvelet coefficients. In the present work an algorithm for image fusion based on the curvelet transform was implemented, analyzed, and compared with a wavelet-based fusion algorithm. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the wavelet-based image fusion algorithms.

Medical image fusion based on modified pulse coupled neural network model and kirsch operator

2019 ◽  
Vol 17 (06) ◽  
pp. 1950052 ◽  
Author(s):  
Guofen Wang ◽  
Yongdong Huang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Feature Maps ◽  
Fusion Algorithm ◽  
Significant Information ◽  
Medical Image Fusion ◽  
Fused Image ◽  
Weight Coefficients

The medical image fusion process integrates the information of multiple source images into a single image. This fused image can provide more comprehensive information and is helpful in clinical diagnosis and treatment. In this paper, a new medical image fusion algorithm is proposed. Firstly, the original image is decomposed into a low-frequency sub-band and a series of high-frequency sub-bands by using nonsubsampled shearlet transform (NSST). For the low-frequency sub-band, kirsch operator is used to extract the directional feature maps from eight directions and novel sum-modified-Laplacian (NSML) method is used to calculate the significant information of each directional feature map, and then, combining a sigmod function and the significant information updated by gradient domain guided image filtering (GDGF), calculate the fusion weight coefficients of the directional feature maps. The fused feature map is obtained by summing the convolutions of the weight coefficients and the directional feature maps. The final fused low-frequency sub-band is obtained by the linear combination of the eight fused directional feature maps. The modified pulse coupled neural network (MPCNN) model is used to calculate the firing times of each high-frequency sub-band coefficient, and the fused high-frequency sub-bands are selected according to the firing times. Finally, the inverse NSST acts on the fused low-frequency sub-band and the fused high-frequency sub-bands to obtain the fused image. The experimental results show that the proposed medical image fusion algorithm expresses some advantages over the classical medical image fusion algorithms in objective and subjective evaluation.

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.

scholarly journals A Critical Analysis of the Multi-Focus Images Fusion Based on Discrete Wavelet Transform and Computer Vision

2021 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel
Keyword(s):  
Computer Vision ◽  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Image Information ◽  
Depth Analysis ◽  
Fused Image ◽  
Focus Image

Abstract This paper focused on the challenge of image fusion processing and lack of reliable image information and proposed multi-focus image fusion using discrete wavelet transforms and computer vision techniques for the fused image coefficient selection process. I made an in-depth analysis and improvement on the existing algorithms from the wavelet transform and the rules of multi-focus image fusion object features’ extractions. The wavelet transform uses authentic localization properties, and computer vision provides efficient processing time and is a powerful method to analyze object focus in the high-frequency precision and steps. The process of image fusion using wavelet transformation is the wavelet basis function and wavelet decomposition level in iterative experiments to enhance fused image information. The rules of multi-focus image fusions are the wavelet transformation on the features of the high-frequency coefficients, which enhance the fusion image features reliability on the frequency domain and regional contrast of the object.

A Novel Multi-Focus Image Fusion Method Using Shearlet Transform

2010 ◽  
Vol 121-122 ◽  
pp. 373-378 ◽  
Author(s):  
Jia Zhao ◽  
Li Lü ◽  
Hui Sun
Keyword(s):  
Image Fusion ◽  
Fusion Rule ◽  
Fusion Method ◽  
Rule Based ◽  
Shearlet Transform ◽  
Fused Image ◽  
Selection Principles ◽  
Focus Image ◽  
Image Fusion Method ◽  
Traditional Image

According to the different frequency areas decomposed by shearlet transform, the selection principles of the lowpass subbands and highpass subbands were discussed respectively. The lowpass subband coefficients of the fused image can be obtained by means of the fusion rule based on the region variation, the highpass subband coefficients can be selected by means of the fusion rule based on the region energy. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.

scholarly journals Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ling Tan ◽  
Xin Yu
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Medical Image ◽  
Information Transfer ◽  
Medical Images ◽  
Low Frequency ◽  
Source Image ◽  
Fusion Algorithm ◽  
Shearlet Transform

Clinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the problem of poor clarity of edge details that is conducive to maintaining the details of source image in current algorithms. Firstly, the source image is decomposed into low-frequency coefficients and high-frequency coefficients by FFST. Secondly, the K-SVD method is used to train the low-frequency coefficients to obtain the overcomplete dictionary D, and then the OMP algorithm sparsely encodes the low-frequency coefficients to complete the fusion of the low-frequency coefficients. Then, a high-frequency coefficient is applied to excite a pulse-coupled neural network, and the fusion coefficient of the high-frequency coefficient is selected according to the number of ignitions. Finally, the fused low-frequency coefficient and high-frequency coefficient are reconstructed into the fused medical image by FFST inverse transform. The experimental results show that the image fusion result of the proposed algorithm is about 35% higher than the comparison algorithms for the edge information transfer factor QAB/F index and has achieved good results in both subjective visual effects and objective evaluation indicators.

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.

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