Multi-Focus Image Fusion Based on Pyramid Decomposition

2014 ◽  
Vol 672-674 ◽  
pp. 1954-1957
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Guang Liang Cheng
Keyword(s):  
Standard Deviation ◽  
Image Fusion ◽  
Objective Evaluation ◽  
Decomposition Algorithm ◽  
Fusion Process ◽  
Evaluation Index ◽  
Laplacian Pyramid ◽  
Matlab Simulation ◽  
Fusion Algorithm ◽  
Focus Image

Image fusion algorithm is very important in image fusion process. Image fusion algorithm based on pyramid decomposition was reviewed in this paper. Pyramid decomposition algorithm mainly includes Contrast pyramid, Gradient Pyramid, Laplacian Pyramid and Ratio Pyramid. The fusion algorithms based on pyramid decomposition were respectively applied in multi-focus images, advantage and disadvantage were summarized and application was given. Fusion results were given by MATLAB simulation. Objective evaluation index including of mean, standard deviation, entropy and average gradient was calculated in this paper. Image fusion algorithm should be selected according to the information extracted and the aim of fusion.

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.

scholarly journals Multi-focus Image Fusion Based on Random Walk

2021 ◽  
Author(s):  
Zhaobin Wang ◽  
Ziye Wang ◽  
Zijing Cui ◽  
Lina Chen ◽  
Yaonan Zhang
Keyword(s):  
Random Walk ◽  
Image Fusion ◽  
Objective Evaluation ◽  
Input Image ◽  
Experimental Results ◽  
Guided Filter ◽  
Smoothing Effect ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Focus Image

AbstractAn effective multi-focus image fusion algorithm based on random walk is proposed in this paper. Random walk and guided filter have attracted extensive attention in image fusion. Random walk is usually used to solve probability problems and it has a good smoothing effect, and guided filter can preserve the gradient information of the image well. The combination of two algorithms can better retain the edge information of the input image. Six sets of source images and five existing methods are used in the experiment and the experimental results show that the proposed algorithm outperforms the existing methods in both subjective and objective evaluation.

Image Fusion Algorithm Based on Contrast Pyramid and its Performance Evaluation

2014 ◽  
Vol 525 ◽  
pp. 711-714 ◽  
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Jie Li
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Objective Evaluation ◽  
Image Simulation ◽  
Fusion Algorithm ◽  
Reconstruction Process ◽  
Creation Process ◽  
Focus Image ◽  
Simulation Results ◽  
Contrast Information

Contrast pyramid algorithm is put forward in this paper. The human visual system is sensitive to contrast information of image, so contrast pyramid algorithm would outstanding the contrast of image. The algorithm consists of creation process of Gauss Pyramid, the process of creating contrast Pyramid and reconstruction process of clear image. Simulation by MATLAB was completed in multi-focus image, multi-modality image and color image. Objective evaluation index such as mean, standard deviation, entropy and average gradient was calculated Simulation results and index show that the contrast pyramid algorithm has advantage of projecting the contrast of image, especially in color image fusion.

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.

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.

Optimal fusion of multi-focus image: Integrating WNMF and focal point analysis

2021 ◽  
pp. 2150156
Author(s):  
Shi-Hong Zhang ◽  
Qi-Yuan Zhan ◽  
Wen-Yu Li ◽  
Qiong-Ze Wang
Keyword(s):  
Image Fusion ◽  
Point Spread Function ◽  
Focal Point ◽  
Nonnegative Matrix ◽  
Fusion Algorithm ◽  
Point Position ◽  
Position Analysis ◽  
Point Spread ◽  
Spread Function ◽  
Focus Image

Image fusion can be used to improve the image utilization, spatial resolution and spectral resolution, which has been widely applied on medicine, remote sensing, computer vision, weather forecast and military target recognition. The goal of image fusion is to reduce the uncertainty and redundancy of the output and increase the reliability of the image on the basis of the maximum combination of relevant information. In this paper, a multi-focus image fusion algorithm based on WNMF and Focal point position analysis is proposed to improve the image fusion method based on nonnegative matrix factorization. In the imaging process, the Gaussian function is used to approximate the point spread function in the optical system. Then calculate the difference between the original image and the approximate point spread function and get the weighted matrix [Formula: see text]. Finally, we apply the weighted nonnegative matrix algorithm to image fusion, and the new fusion image with clear parts is obtained. Experimental results show that the multi-focus image fusion algorithm based on WNMF and Focal point position analysis (MFWF) is better.

scholarly journals A Multi-Focus Image Fusion Algorithm Based on Contrast Pyramids

2016 ◽  
Author(s):  
Mingzhu Lai ◽  
Jianguo Sun ◽  
Liguo Zhang ◽  
Yiran Shen ◽  
Qing Yang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Region Growing ◽  
Fusion Algorithm ◽  
New Approach ◽  
Focus Image ◽  
Focus Information ◽  
Robust To Noise

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

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