Multi-Focus Image Fusion Algorithm Based on Fast Finite Shearlet Transform and Guided Filter

2018 ◽  
Vol 55 (1) ◽  
pp. 011001
Author(s):  
朱达荣 Zhu Darong ◽  
许露 Xu Lu ◽  
汪方斌 Wang Fangbin ◽  
刘涛 Liu Tao ◽  
储朱涛 Chu Zhutao
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Focus Image

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.

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.

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.

Guided filter-based multi-focus image fusion through focus region detection

2019 ◽  
Vol 72 ◽  
pp. 35-46 ◽  
Author(s):  
Xiaohua Qiu ◽  
Min Li ◽  
Liqiong Zhang ◽  
Xianjie Yuan
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Region Detection ◽  
Focus Image

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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