The influence of low light level ICCD image on low light level and infrared image fusion

2016 ◽  
Author(s):  
Qing-ping Hu ◽  
Xiao-hui Zhang ◽  
Chao Liu
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Light Level ◽  
Low Light

A robust color image fusion for low light level and infrared images

2016 ◽  
Author(s):  
Chao Liu ◽  
Xiao-hui Zhang ◽  
Qing-ping Hu ◽  
Yong-kang Chen
Keyword(s):  
Image Fusion ◽  
Color Image ◽  
Light Level ◽  
Infrared Images ◽  
Low Light

Infrared and Low-light-level Visible Image Fusion Algorithm Based on Contrast Enhancement and Cauchy Fuzzy Function

2019 ◽  
Vol 48 (6) ◽  
pp. 610001
Author(s):  
江泽涛 JIANG Ze-tao ◽  
何玉婷 HE Yu-ting ◽  
张少钦 ZHANG Shao-qin
Keyword(s):  
Image Fusion ◽  
Contrast Enhancement ◽  
Light Level ◽  
Fusion Algorithm ◽  
Low Light ◽  
Visible Image ◽  
Fuzzy Function

Contrast Enhanced Low-light Visible and Infrared Image Fusion

2016 ◽  
Vol 66 (3) ◽  
pp. 266 ◽  
Author(s):  
Sandhya Kumari Teku ◽  
S. Koteswara Rao ◽  
I. Santhi Prabha
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Principal Component ◽  
Bistable System ◽  
Low Light ◽  
Decomposition Approach ◽  
Multiple Modalities ◽  
Multi Scale ◽  
Contrast Enhanced ◽  
Infrared Cameras

<p>Multi-modal image fusion objective is to combine complementary information obtained from multiple modalities into a single representation with increased reliability and interpretation. The images obtained from low-light visible cameras containing fine details of the scene and infrared cameras with high contrast details are the two modalities considered for fusion. In this paper, the low-light images with low target contrast are enhanced by using the phenomenon of stochastic resonance prior to fusion. Entropy is used as a measure to tune iteratively the coefficients using bistable system parameters. The combined advantage of multi scale decomposition approach and principal component analysis is utilized for the fusion of enhanced low-light visible and infrared images. Experimental results were carried out on different image datasets and analysis of the proposed methods were discussed. </p>

Real-time color image fusion for infrared and low-light-level cameras

2009 ◽  
Author(s):  
Junju Zhang ◽  
Yiyong Han ◽  
Benkang Chang ◽  
Yihui Yuan ◽  
Yunsheng Qian ◽  
...  
Keyword(s):  
Image Fusion ◽  
Real Time ◽  
Color Image ◽  
Light Level ◽  
Low Light

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

Infrared and Low-Light-Level Image Fusion Method Based on Sparse Representation and Color Transfer

2014 ◽  
Vol 511-512 ◽  
pp. 462-466
Author(s):  
Shi Hong Xu ◽  
Guo Qing Huang ◽  
Cun Chao Liu ◽  
Chun Ping Xiong
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Color Image ◽  
Light Level ◽  
Color Contrast ◽  
Fusion Method ◽  
Color Transfer ◽  
Low Light ◽  
Common Component ◽  
Natural Color

A natural color fusion method for infrared and low-light-level image is proposed. This method utilizes image fusion and color transfer. The fused image uses sparse representation to merge the source images information to be assigned to the Y channel. And then the I and Q channel is combined using Toets method, which extracts the common component from the source images. Finally, the false-color image is obtained by using color transfer technology to the prior pseudo-color YIQ image. Experiments show that the result of our method is information that is more salient, has a higher color contrast, and a more natural color appearance when compared with those produced by the traditional coloration algorithm.

Image Fusion Algorithms for True Color Low Light Level Night Vision

2019 ◽  
Vol 56 (8) ◽  
pp. 081008
Author(s):  
蒋云峰 Jiang Yunfeng ◽  
武东生 Wu Dongsheng ◽  
黄富瑜 Huang Fuyu
Keyword(s):  
Image Fusion ◽  
Light Level ◽  
Night Vision ◽  
Low Light ◽  
True Color
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