Fuzzy Transform for Image Fusion and Edge Detection

2020 ◽  
pp. 61-79
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
Fuzzy Transform

A Framework for Edge Detection and Linking Using Wavelets and Image Fusion

2008 ◽  
Author(s):  
Pervez Akhtar ◽  
T.J. Ali ◽  
M.I. Bhatti ◽  
M.A. Muqeet
Keyword(s):  
Edge Detection ◽  
Image Fusion

A multi-focus color image fusion method based on edge detection

2015 ◽  
Author(s):  
Wang Yajie ◽  
Ye Yongsheng ◽  
Ran Xiaoyan ◽  
Wu Yanyan ◽  
Shi Xiangbin
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
Color Image ◽  
Fusion Method ◽  
Image Fusion Method

scholarly journals Image Fusion Algorithm at Pixel Level Based on Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiming Chen ◽  
Liping Chen ◽  
Mohammad Shabaz
Keyword(s):  
Edge Detection ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Region ◽  
Experimental Comparison ◽  
Fusion Algorithm ◽  
High Frequency Region ◽  
Running Time ◽  
Low Efficiency

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.

scholarly journals Enhanced image fusion using directional contrast rules in fuzzy transform domain

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Amita Nandal ◽  
Hamurabi Gamboa Rosales
Keyword(s):  
Image Fusion ◽  
Transform Domain ◽  
Fuzzy Transform
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