Research and Development of Multi-Scale to Pixel-Level Image Fusion

2013 ◽  
Vol 448-453 ◽  
pp. 3625-3628
Author(s):  
Ming Jing Li ◽  
Xiao Li Wang ◽  
Yu Bing Dong
Keyword(s):  
Image Fusion ◽  
Fusion Process ◽  
Transform Domain ◽  
Fusion Method ◽  
Original Image ◽  
Fusion Algorithm ◽  
Image Domain ◽  
Multi Scale ◽  
Imaging Characteristics ◽  
Fusion Methods

Image fusion method based on image multi-scale decomposition is a kind of fusion method of multi-scale, multi-resolution image fusion. Its fusion process realize in different scales and different spatial resolution and different decomposition layer. Fusion effects based on multi-scale decomposition algorithm can obviously improve compared to the simple fusion methods. Among the fusion algorithm based on multi-scale to pixel-level image fusion, Pyramid decomposition and wavelet decomposition are widely used, the original image is decomposed to convert the original image domain to transform domain, and then, fusion process realized in transform domain according to certain rules of image fusion. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

Research and Development of Non Multi-Scale to Pixel-Level Image Fusion

2013 ◽  
Vol 448-453 ◽  
pp. 3621-3624 ◽  
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Image Fusion ◽  
Basic Principle ◽  
Weighted Average ◽  
Source Image ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Imaging Characteristics ◽  
Fusion Methods ◽  
Image Fusion Method

Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a new image. Simple pixel level image fusion method mainly includes the pixel gray value being average or weighted average, pixel gray value being selected large and pixel gray value being selected small, etc. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.

Research on Image Fusion Based on Pyramid Decomposition

2013 ◽  
Vol 860-863 ◽  
pp. 2855-2858
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Image Fusion ◽  
Fusion Method ◽  
Laplacian Pyramid ◽  
Fusion Algorithm ◽  
Resolution Method ◽  
Multi Scale ◽  
Simulation Results ◽  
Spatial Fusion

Different objects could be analyzed by use of pyramid decomposition of image. Image fusion algorithm based on pyramid decomposition of image is multi-scale, multi-resolution method. Its process is completed on different scale, different resolution and different decomposition layer. Compared with spatial fusion method, fusion effects improve obviously. In this paper, principal of pyramid decomposition and process were introduced, and simulation results of image fusion based on Laplacian pyramid, gradient pyramid, ration pyramid and contrast shows that image fusion based on pyramid decomposition is improve obviously.

scholarly journals An Image Fusion Algorithm based on Modified Contourlet Transform

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Image Fusion ◽  
Finite Depth ◽  
Contourlet Transform ◽  
Translation Invariance ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Directional Filter ◽  
Fusion Methods ◽  
Focus Image ◽  
Combining Information

Multi-focus image fusion has established itself as a useful tool for reducing the amount of raw data and it aims at overcoming imaging cameras’ finite depth of f ield by combining information from multiple images with the same scene. Most of existing fusion algorithms use the method of multi-scale decompositions (MSD) to fuse the s ource images. MSD-based fusion algorithms provide much better performance than the conventional fusion methods .In the image fusion algorithm based on multi-scale decomposition, how to make full use of the characteristics of coefficients to fuse images is a key problem.This paper proposed a modified contourlet transform(MCT) based on wavelets and nonsubsampled directional filter banks(NSDFB). The image is decomposed in wavelet domain,and each highpass subband of wavelets is further decomposed into multiple directional subbands by using NSDFB. The MCT has the important features of directionality and translation invariance. Furthermore, the MCT and a novel region energy strategy are exploited to perform image fusion algorithm. simulation results shows that the proposed method can the fusion results visually and also improve in objective evaluating parameters.

Image Fusion Algorithm Based on Gradient Pyramid and its Performance Evaluation

2014 ◽  
Vol 525 ◽  
pp. 715-718 ◽  
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Performance Evaluation ◽  
Standard Deviation ◽  
Image Fusion ◽  
Color Image ◽  
Decomposition Algorithms ◽  
Original Image ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Focus Image ◽  
Simulation Results

Image fusion algorithm based on gradient pyramid is one of the multi-scale, multi-resolution decomposition algorithms. Original image was decomposed into Gauss pyramid, after that, gradient decomposition was completed on each layer in four directions, and fusion effect was evaluated by taking using of entropy, average gradient, mean and standard deviation. Simulation results show that gradient pyramid algorithm is effective to multi-focus image and color image.

A Novel Image Fusion Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 774-776 ◽  
pp. 1528-1531
Author(s):  
Xiu Ming Sun ◽  
Zhi Min Wang ◽  
Peng Geng
Keyword(s):  
Image Fusion ◽  
Visual Information ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Multi Scale ◽  
Fusion Methods ◽  
Shift Invariance

The nonsubsampled contourlet transform (NSCT) is not only with multi-scale and localization, but also with multi-direction, anisotropy and shift-invariance. A novel image fusion algorithm based on the NSCT is proposed in this paper, aiming at solving the fusion problem of images. Experimental results demonstrate that the proposed algorithm cannot only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. It outperforms the traditional discrete wavelet transform-based and the Contourlet-based image fusion methods in terms of both visual performance and objective evaluation.

Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain

2020 ◽  
Vol 31 (01) ◽  
pp. 2050050 ◽  
Author(s):  
Bo Li ◽  
Hong Peng ◽  
Xiaohui Luo ◽  
Jun Wang ◽  
Xiaoxiao Song ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Images ◽  
Low Frequency ◽  
Transform Domain ◽  
Fusion Method ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Model Combining ◽  
Fusion Methods ◽  
Image Fusion Method

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

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