Multi-focus Image Fusion Algorithm Based on Extended Phase Stretch Transform

2020 ◽  
Vol 57 (22) ◽  
pp. 221007
Author(s):  
张亚峰 Zhang Yafeng ◽  
耿则勋 Geng Zexun ◽  
王军敏 Wang Junmin
Keyword(s):  
Image Fusion ◽  
Fusion Algorithm ◽  
Extended Phase ◽  
Focus Image ◽  
Phase Stretch Transform

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.

Multi-Focus Image Fusion Using Local Energy Pattern

2011 ◽  
Vol 145 ◽  
pp. 119-123
Author(s):  
Ko Chin Chang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Local Energy ◽  
View Point ◽  
Fusion Algorithm ◽  
Image Capture ◽  
Focus Image ◽  
Better Than

For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.

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