scholarly journals Correction: Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249546
Author(s):  
Keyword(s):  
Image Enhancement ◽  
Anisotropic Diffusion ◽  
Infrared Image ◽  
Fusion Algorithm

scholarly journals Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement (capitalize only the first word in a title (or heading), the first word in a subtitle (or subheading), and any proper nouns)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245563
Author(s):  
Hui Huang ◽  
Linlu Dong ◽  
Zhishuang Xue ◽  
Xiaofang Liu ◽  
Caijian Hua
Keyword(s):  
Image Enhancement ◽  
Anisotropic Diffusion ◽  
Infrared Image ◽  
Source Image ◽  
Laplacian Pyramid ◽  
Infrared Images ◽  
Fusion Algorithm ◽  
Public Data ◽  
Visible Images ◽  
Enhancement Algorithm

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.

Gas cloud infrared image enhancement based on anisotropic diffusion

2011 ◽  
Author(s):  
Jiakun Li ◽  
Lingxue Wang ◽  
Changxing Zhang ◽  
Yunting Long ◽  
Bei Zhang
Keyword(s):  
Image Enhancement ◽  
Anisotropic Diffusion ◽  
Infrared Image ◽  
Gas Cloud

Application of Wavelet Transforms Method to Infrared Image Enhancement

2013 ◽  
Vol 760-762 ◽  
pp. 1529-1533
Author(s):  
Wei Chang Xu ◽  
Tao Tang ◽  
Ji Fang Liu ◽  
Wei Huang
Keyword(s):  
Image Enhancement ◽  
Wavelet Transforms ◽  
Infrared Image

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

Improved multi-scale Retinex infrared image enhancement

2021 ◽  
Vol 36 (3) ◽  
pp. 465-474
Author(s):  
Ran-ran WEI ◽  
◽  
Wei-da ZHAN ◽  
De-peng ZHU ◽  
Yong TIAN
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Multi Scale
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