Polarization image fusion algorithm based on improved PCNN

2013 ◽  
Author(s):  
Siyuan Zhang ◽  
Yan Yuan ◽  
Lijuan Su ◽  
Liang Hu ◽  
Hui Liu
Keyword(s):  
Image Fusion ◽  
Fusion Algorithm ◽  
Polarization Image

A visible polarization image fusion algorithm based on NSST transform

2020 ◽  
Author(s):  
Zhao Zhen Jiang ◽  
Yusheng Han ◽  
Fei Ye ◽  
Shuaijun Ren ◽  
Hao Zhai ◽  
...  
Keyword(s):  
Image Fusion ◽  
Fusion Algorithm ◽  
Polarization Image

Fusion of Polarization Image Based on Curvelet Transform

2014 ◽  
Vol 536-537 ◽  
pp. 111-114 ◽  
Author(s):  
De Xiang Zhang ◽  
Hong Hai Wang ◽  
Feng Xue
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Fusion Algorithm ◽  
Information Measurement ◽  
Multi Scale ◽  
Fused Image ◽  
Polarization Image ◽  
Multi Scales

Curvelet transform is the combination of the multi-scale analysis and multi-directional analysis transforms, which is more suitable for objects with curves. Applications of the curvelet transform have increased rapidly in the field of image fusion. Firstly, using the curvelet transform, several polarization images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused curvelet coefficients. In the present work an algorithm for image fusion based on the curvelet transform was implemented, analyzed, and compared with a wavelet-based fusion algorithm. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the wavelet-based image fusion algorithms.

Polarization Image Fusion Algorithm Using NSCT and CNN

2021 ◽  
Author(s):  
Shifeng Wang ◽  
Jin Meng ◽  
Yuan Zhou ◽  
Qinglong Hu ◽  
Zhiwei Wang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Fusion Algorithm ◽  
Polarization Image

scholarly journals Optimization Algorithm of Infrared-Polarization Image Fusion Based on Fireworks Algorithm

2021 ◽  
Author(s):  
Zhiyong Yang ◽  
Gaoxiang Lu ◽  
Wei Cai ◽  
Danqiu Qiao ◽  
Junchen Song
Keyword(s):  
Image Fusion ◽  
Optimization Algorithm ◽  
Fusion Algorithm ◽  
Problem Model ◽  
Fireworks Algorithm ◽  
Long Wave ◽  
Polarization Image ◽  
Infrared Intensity ◽  
Intensity Image ◽  
Long Wave Infrared

Abstract Because of the shortcomings of traditional infrared-polarization image fusion algorithm, such as low intelligence and single optimization index, this paper proposes an intelligent infrared-polarization image fusion optimization algorithm based on fireworks algorithm. Firstly, an improved differential image correction method based on single pixel nonuniformity is proposed to remove the cold reflection. The two-dimensional discrete cosine transform (DCT) is used to reduce the image sensitivity and improve the robustness, and the Stokes vector formula is used to obtain the polarization characteristic image. Secondly, based on the strong complementarity between infrared-intensity image and degree of linear-polarization (DOLP) image and the explosive optimization of fireworks algorithm, the problem model of weighted fusion algorithm is established, and the fitness function based on root mean square error (RMSE) is constructed to calculate the optimal weight of source image. In the fusion experiment of long-wave infrared-intensity image and DOLP image, this method is compared with the common fusion algorithms. The results show that this method can effectively fuse the infrared-intensity and degree of polarization information, and the evaluation indexes of standard deviation, spatial frequency, mutual information, structural similarity, peak signal-to-noise ratio and information entropy of the fusion image are better than the comparison algorithm. In the future, cooperated with the long-wave infrared-polarization imaging system, this method can be applied to improve the infrared detection ability in complex environment.

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
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