Adaptive polarization image fusion based on regional energy dynamic weighted average

2005 ◽  
Vol 1 (3) ◽  
pp. 224-227 ◽  
Author(s):  
Yong-qiang Zhao ◽  
Quan Pan ◽  
Hong-cai Zhang
Keyword(s):  
Image Fusion ◽  
Weighted Average ◽  
Regional Energy ◽  
Polarization Image

NSCT-Based Multi-Sensor Image Fusion Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3212-3216
Author(s):  
Hai Feng Tan ◽  
Wen Jie Zhao ◽  
De Jun Li ◽  
Tian Wen Luo
Keyword(s):  
Image Fusion ◽  
Average Method ◽  
Low Frequency ◽  
Weighted Average ◽  
Inverse Transformation ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Focus Image ◽  
Quantitative Indicators

Against the defects that the favoritism method and average method in the multi-sensor image fusion are apt to impair the image contrast, an image fusion algorithm based on NSCT is proposed. Firstly, this algorithm applied NSCT to the rectified multi-sensor images from the same scene, then different fusion strategies were adopted to fuse the low-frequency and high-frequency directional sub-band coefficients respectively: regional energy adaptive weighted method was used for low-frequency sub-band coefficient; the directional sub-band coefficient adopted a regional-energy-matching program that combined weighted average method and selection method. Finally, the fusion image was obtained by NSCT inverse transformation. Experiments were conducted to IR and visible light image and multi-focus image respectively. And the fusion image was evaluated objectively. The experimental results show that the fusion image obtained through this algorithm has better subjective visual effects and objective quantitative indicators. It is also superior to the traditional fusion method.

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

2021 ◽  
Vol 12 (4) ◽  
pp. 78-97
Author(s):  
Hassiba Talbi ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Weighted Average ◽  
Optimization Method ◽  
Absolute Maximum ◽  
Complex Wavelet Transform ◽  
Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

Method of Underwater Polarization Image Fusion Based on Hierarchical and Multi-scale Transform

2014 ◽  
Vol 43 (5) ◽  
pp. 510004
Author(s):  
王慧斌 WANG Hui-bin ◽  
廖艳 LIAO Yan ◽  
沈洁 SHEN Jie ◽  
王鑫 WANG Xin
Keyword(s):  
Image Fusion ◽  
Multi Scale ◽  
Polarization Image

Underwater Polarization Image Fusion Based on NSST and Adaptive SPCNN

2020 ◽  
Vol 57 (6) ◽  
pp. 061006
Author(s):  
于津强 Yu Jinqiang ◽  
段锦 Duan Jin ◽  
陈伟民 Chen Weimin ◽  
莫苏新 Mo Suxin ◽  
李英超 Li Yingchao ◽  
...  
Keyword(s):  
Image Fusion ◽  
Polarization Image

Pixel-Level Image Fusion Based on Programmable GPU

2013 ◽  
Vol 347-350 ◽  
pp. 3872-3876
Author(s):  
Hong Li ◽  
Gao Feng Tang ◽  
Fen Xia Wu ◽  
Cong E Tan
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Parallel Algorithm ◽  
Rapid Development ◽  
General Purpose ◽  
Kernel Functions ◽  
Regional Energy ◽  
Novel Algorithm ◽  
Evaluation Accuracy

A novel algorithm which is image fusion based on GPU is proposed. The fused rule is regional energy. In recent years, the power of the computing of GPU has been greatly improved, which results that using it for the general-purpose computing has a rapid development. The essay researches on implementing the oriental field algorithm on GPU, including selecting GPU memories and dividing blocks and threads of GPU kernel functions. The results of experiment on the GPU of NVIDIA GTX560 are given, which shows that our proposed algorithm can be applied to the field of image fusion. Experiment shows the proposed algorithm has faster calcu-lation velocity and higher evaluation accuracy. The speed of the parallel algorithm is 200 times faster than that of the CPU-based implementation. Meanwhile the mutual information and QAB/F parameters are higher than that of the CPU-based algorithm.

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