Visual and infrared image fusion algorithm based on adaptive PCNN

2017 ◽  
Author(s):  
Yajun Song ◽  
Chen Yang ◽  
Zhi Chai ◽  
Jinbao Yang
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Fusion Algorithm

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

NPP/Visible Infrared Image Radiometer Suite Low-Light Image Fusion Algorithm for City Lights in Mid-Eastern China

2018 ◽  
Vol 55 (10) ◽  
pp. 102804
Author(s):  
余越 Yu Yue ◽  
胡秀清 Hu Xiuqing ◽  
闵敏 Min Min ◽  
许廷发 Xu Tingfa ◽  
何玉青 He Yuqing ◽  
...  
Keyword(s):  
Image Fusion ◽  
Eastern China ◽  
Infrared Image ◽  
Fusion Algorithm ◽  
Low Light ◽  
Light Image

A GAN-based visible and infrared image fusion algorithm

2021 ◽  
Author(s):  
Hongzhi Zhang ◽  
Yifan Shen ◽  
Yangyan Ou ◽  
Bo Ji ◽  
Jia He
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Fusion Algorithm

Spectrum Characteristics Preserved Visible and Near-Infrared Image Fusion Algorithm

2021 ◽  
Vol 23 ◽  
pp. 306-319 ◽  
Author(s):  
Zhuo Li ◽  
Hai-Miao Hu ◽  
Wei Zhang ◽  
Shiliang Pu ◽  
Bo Li
Keyword(s):  
Image Fusion ◽  
Near Infrared ◽  
Infrared Image ◽  
Fusion Algorithm ◽  
Spectrum Characteristics

scholarly journals Multi-sensor Image Fusion Algorithm Based on Multiresolution Analysis

2018 ◽  
Vol 14 (06) ◽  
pp. 44 ◽  
Author(s):  
Zhi-guo Wang ◽  
Wei Wang ◽  
Baolin Su
Keyword(s):  
Image Fusion ◽  
Quality Evaluation ◽  
Color Image ◽  
Infrared Image ◽  
Principal Component ◽  
Infrared Images ◽  
Fusion Algorithm ◽  
Time Intervals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

<p class="0abstract">To solve the fusion problem of visible and infrared images, based on image fusion algorithm such as region fusion, wavelet transform, spatial frequency, Laplasse Pyramid and principal component analysis, the quality evaluation index of image fusion was defined. Then, curve-let transform was used to replace the wavelet change to express the superiority of the curve. It integrated the intensity channel and the infrared image, and then transformed it to the original space to get the fused color image. Finally, two groups of images at different time intervals were used to carry out experiments, and the images obtained after fusion were compared with the images obtained by the first five algorithms, and the quality was evaluated. The experiment showed that the image fusion algorithm based on curve-let transform had good performance, and it can well integrate the information of visible and infrared images. It is concluded that the image fusion algorithm based on curve-let change is a feasible multi-sensor image fusion algorithm based on multi-resolution analysis. </p>

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