Image fusion in infrared image and visual image using normalized mutual information

2008 ◽  
Author(s):  
Changhan Park ◽  
Kyung-hoon Bae ◽  
Sungnam Choi ◽  
Jik-Han Jung
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Infrared Image ◽  
Visual Image ◽  
Normalized Mutual Information

scholarly journals An Objective Non-Reference Metric Based on Arimoto Entropy for Assessing the Quality of Fused Images

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 879 ◽  
Author(s):  
Bicao Li ◽  
Runchuan Li ◽  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Zongmin Wang
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Principal Component ◽  
Laplacian Pyramid ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Normalized Mutual Information ◽  
Guided Filtering ◽  
Reference Measure

In the technologies, increasing attention is being paid to image fusion; nevertheless, how to objectively assess the quality of fused images and the performance of different fusion algorithms is of significance. In this paper, we propose a novel objective non-reference measure for evaluating image fusion. This metric employs the properties of Arimoto entropy, which is a generalization of Shannon entropy, measuring the amount of information that the fusion image contains about two input images. Preliminary experiments on multi-focus images and multi-modal images using the average fusion algorithm, contrast pyramid, principal component analysis, laplacian pyramid, guided filtering and discrete cosine transform have been implemented. In addition, a comparison has been conducted with other relevant quality metrics of image fusion such as mutual information, normalized mutual information, Tsallis divergence and the Petrovic measure. The experimental results illustrate that our presented metric correlates better with the subjective criteria of these fused images.

Real-Time Registration Architecture for Far Infrared and Visible Image Using Mutual Information

2010 ◽  
Vol 129-131 ◽  
pp. 328-332 ◽  
Author(s):  
Wen Gang Qin ◽  
Ai Hua Gao
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Real Time ◽  
Infrared Image ◽  
Far Infrared ◽  
Processing Unit ◽  
Visible Image ◽  
Field Programmable ◽  
Hardware Resource ◽  
Computationally Intensive

Image registration based on mutual information which shown to be effective is a computationally intensive process, and thus difficult to implement in real-time. A real-time hardware platform for visible and infrared image fusion is presented. The architecture takes field programmable gate array (FPGA) as its main processor, and integrates both kernel algorithm and peripheral management on its. Real-time performance can be achieved by optimizing hardware resource and algorithm, alone with memory access parallelization and processing unit execute pipelining. The analysis result shows hardware architecture can evaluate image fusion for 160x120 arrays image less than 36ms, and fusion image improves the target detection and recognition probability.

Infrared Image Fusion Algorithm Selection Based on Joint Drop Shadow of Possibility Distributions

2021 ◽  
Vol 50 (4) ◽  
pp. 228-240
Author(s):  
吉琳娜 Linna JI ◽  
郭小铭 Xiaoming GUO ◽  
杨风暴 Fengbao YANG ◽  
张雅玲 Yaling ZHANG
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Fusion Algorithm ◽  
Algorithm Selection ◽  
Possibility Distributions
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