scholarly journals Neighbour Local Variability for Multi-Focus Images Fusion

2020 ◽  
Vol 11 (6) ◽  
pp. 37-51
Author(s):  
Ias Sri Wahyuni ◽  
Rachid Sabre
Keyword(s):  
Image Fusion ◽  
New Method ◽  
Single Image ◽  
Edge Function ◽  
Focus Image ◽  
Local Variability ◽  
Region Size

The goal of multi-focus image fusion is to integrate images with different focus objects in order to obtain a single image with all focus objects. In this paper, we give a new method based on neighbour local variability (NLV) to fuse multi-focus images. At each pixel, the method uses the local variability calculated from the quadratic difference between the value of the pixel and the value of all pixels in its neighbourhood. It expresses the behaviour of the pixel with respect to its neighbours. The variability preserves the edge function because it detects the sharp intensity of the image. The proposed fusion of each pixel consists of weighting each pixel by the exponential of its local variability. The quality of this fusion depends on the size of the neighbourhood region considered. The size depends on the variance and the size of the blur filter. We start by modelling the value of the neighbourhood region size as a function of the variance and the size of the blur filter. We compare our method to other methods given in the literature. We show that our method gives a better result.

scholarly journals Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

2021 ◽  
Author(s):  
Ias Sri Wahyuni ◽  
Rachid Sabre
Keyword(s):  
Image Fusion ◽  
Mass Function ◽  
New Method ◽  
Dempster Shafer Theory ◽  
Local Distance ◽  
Focus Image ◽  
Fusion Images ◽  
Shafer Theory ◽  
Quadratic Distance ◽  
Local Variability

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

Image Fusion Algorithm Based on Wavelet Transform and Laplacian Pyramid

2013 ◽  
Vol 860-863 ◽  
pp. 2846-2849
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Xiao Li Wang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Relevant Information ◽  
Experimental Results ◽  
Improve Quality ◽  
Laplacian Pyramid ◽  
Single Image ◽  
Fusion Algorithm ◽  
Fused Image

Image fusion is process which combine relevant information from two or more images into a single image. The aim of fusion is to extract relevant information for research. According to different application and characteristic of algorithm, image fusion algorithm could be used to improve quality of image. This paper complete compare analyze of image fusion algorithm based on wavelet transform and Laplacian pyramid. In this paper, principle, operation, steps and characteristic of fusion algorithm are summarized, advantage and disadvantage of different algorithm are compared. The fusion effects of different fusion algorithm are given by MATLAB. Experimental results shows that quality of fused image would be improve obviously.

scholarly journals Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

2021 ◽  
Vol 38 (2) ◽  
pp. 247-259
Author(s):  
Asan Ihsan Abas ◽  
Nurdan Akhan Baykan
Keyword(s):  
Image Fusion ◽  
Curvelet Transform ◽  
Bat Algorithm ◽  
Single Image ◽  
Image Capture ◽  
Multi Scale ◽  
Optimal Weights ◽  
Fusion Methods ◽  
Low Pass ◽  
Focus Image

Focus is limited and singular in many image capture devices. Therefore, different focused objects at different distances are obtained in a single image taken. Image fusion can be defined as the acquisition of multiple focused objects in a single image by combining important information from two or more images into a single image. In this paper, a new multi-focus image fusion method based on Bat Algorithm (BA) is presented in a Multi-Scale Transform (MST) to overcome limitations of standard MST Transform. Firstly, a specific MST (Laplacian Pyramid or Curvelet Transform) is performed on the two source images to obtain their low-pass and high-pass bands. Secondly, optimization algorithms were used to find out optimal weights for coefficients in low-pass bands to improve the accuracy of the fusion image and finally the fused multi-focus image is reconstructed by the inverse MST. The experimental results are compared with different methods using reference and non-reference evaluation metrics to evaluate the performance of image fusion methods.

Image Matting Based Multi-Focus Image Fusion With Optimal Cluster Size

2018 ◽  
Vol 8 (3) ◽  
pp. 41-65
Author(s):  
Rajesh Dharmaraj ◽  
Christopher Durairaj Daniel Dharmaraj
Keyword(s):  
Image Fusion ◽  
Cluster Size ◽  
Image Matting ◽  
Distance Ratio ◽  
Fused Image ◽  
Fusion Methods ◽  
Focus Image ◽  
Background Pixel ◽  
Optimal Cluster

Image fusion is used to intensify the quality of images by combining two images of same scene obtained from different techniques. The present work deals with the effective extraction of pixel information from the source images that hold the key to multi focus image fusion. A solely vicinity-based image matting algorithm that relies on the close pixel clusters in the input images and their trimap, is presented in this article. The pixel cluster size, N plays a significant role in deciding the identity of the unknown pixel. The distance between each unknown pixel from foreground and background pixel clusters has been computed based on minimum quasi Euclidean distance. The minimum distance ratio gives the alpha value of each unknown pixel in the image. Finally, the focus regions are blend together to obtain the resultant fused image. On perceiving the results visually and objectively, it is concluded that proposed method works better in extracting the focused pixels and improving fusion quality, compared with other existing fusion methods.

Multi-Focus Image Fusion Method via Multiple Wavelet Bases

2013 ◽  
Vol 467 ◽  
pp. 604-608
Author(s):  
Wen Liu An ◽  
Xiao Ling Wang
Keyword(s):  
Image Fusion ◽  
Weighted Average ◽  
New Method ◽  
Wavelet Bases ◽  
Base Method ◽  
Focus Image ◽  
Fusion Images ◽  
Local Gradient ◽  
Image Fusion Method ◽  
Better Than

In this article, a new method for multi-focus image fusion via multiple wavelet bases is proposed.Firstly the wavelet transform is used to perform a multiscale decomposion on each image , then based on local gradient in the fusion to get primary fusion image .And then a set of the primary fusion images are obtained.Next these primary fusion images are fused to obtain final fusion iamge within spatial domain based on local variances weighted average rule.Experimental results show the new method is better than the traditional single wavelet base method in fusion effect.

A Review on Various Techniques of Image Fusion for Quality Improvement of Images

2018 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Aman Saini ◽  
. Pratibha
Keyword(s):  
Quality Improvement ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Single Image ◽  
Lifting Technique ◽  
Focus Image ◽  
Convolution Approach ◽  
Combining Information

Image fusion is a significant topic in perspective processing. Image fusion is a process of mixing the appropriate data from some images into a single image where the resulting merged picture will be more useful and complete than any of the input images. Multi focus Image fusion is procedure of combining information of several imagery of a view and consequently has \everywhere in focus "image. Lifting technique allows faster implementation of wavelet transform. It requires half number of computations as compared to traditional convolution approach.

scholarly journals A New Method of Multi-Focus Image Fusion Using Laplacian Operator and Region Optimization

2018 ◽  
Vol 06 (05) ◽  
pp. 106-118
Author(s):  
Chao Wang ◽  
Rui Yuan ◽  
Yuqiu Sun ◽  
Yuanxiang Jiang ◽  
Changsheng Chen ◽  
...  
Keyword(s):  
Image Fusion ◽  
New Method ◽  
Laplacian Operator ◽  
Focus Image

A New Multi-Focus Image Fusion Algorithm Based on Contrast Ratio and Discrete Wavelet Frame Transform

2012 ◽  
Vol 542-543 ◽  
pp. 1011-1018
Author(s):  
Zheng Hong Deng ◽  
Mei Jing Wang ◽  
Xiao Ping Bai
Keyword(s):  
Image Fusion ◽  
Contrast Ratio ◽  
Source Image ◽  
Discrete Wavelet ◽  
Wavelet Frame ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Focus Image ◽  
Wavelet Frame Transform

This paper proposes a multi-focus image fusion algorithm based on contrast ratio and discrete wavelet frame transform. Firstly, this algorithm uses wavelet transform to perform the wavelet decomposition of the source image, and then obtains the high-frequency sub-band coefficients after the discrete wavelet frame transform to reflect the details of the image, finally, gets the fusion image obtained by wavelet reconstruction. Using evaluation indicators of information entropy, standard deviation, average gradient and spatial frequency, it objectively evaluates the fusion quality of this algorithm. The experimental results show that the quality and effect of the fusion image derived from the algorithm are significantly improved.

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