Multi-focus image fusion based on guided filter and mixed focus measure

2019 ◽  
Author(s):  
Didi Zhao ◽  
Jiahui Li ◽  
Yun Zhou ◽  
Yiqun Ji
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Focus Measure ◽  
Focus Image

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

Multi-focus Image Fusion Based on the Improved PCNN and Guided Filter

2016 ◽  
Vol 45 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Zhaobin Wang ◽  
Shuai Wang ◽  
Ying Zhu
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Focus Image

Multi-focus image fusion using a guided-filter-based difference image

2016 ◽  
Vol 55 (9) ◽  
pp. 2230 ◽  
Author(s):  
Xiang Yan ◽  
Hanlin Qin ◽  
Jia Li ◽  
Huixin Zhou ◽  
Tingwu Yang
Keyword(s):  
Image Fusion ◽  
Guided Filter ◽  
Difference Image ◽  
Focus Image

scholarly journals Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7371
Author(s):  
Jiyoung Lee ◽  
Seunghyun Jang ◽  
Jungbin Lee ◽  
Taehan Kim ◽  
Seonghan Kim ◽  
...  
Keyword(s):  
Image Fusion ◽  
Focal Depth ◽  
Window Size ◽  
Guided Filter ◽  
Image Stack ◽  
Microscopic Images ◽  
Focal Area ◽  
Axial Translation ◽  
Focus Image ◽  
Quality Assessments

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.

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.

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
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