Real-time color transfer system for low-light level visible and infrared images in YUV color space

2007 ◽  
Author(s):  
Lingxue Wang ◽  
Yuanmeng Zhao ◽  
Weiqi Jin ◽  
Shiming Shi ◽  
Shengxiang Wang
Keyword(s):  
Real Time ◽  
Color Space ◽  
Light Level ◽  
Transfer System ◽  
Infrared Images ◽  
Color Transfer ◽  
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

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

Fusion of the low-light-level visible and infrared images for night-vision context enhancement

2018 ◽  
Vol 16 (1) ◽  
pp. 013501 ◽  
Author(s):  
Jin Zhu Jin Zhu ◽  
Weiqi Jin Weiqi Jin ◽  
Li Li Li Li ◽  
Zhenghao Han Zhenghao Han ◽  
Xia Wang Xia Wang
Keyword(s):  
Light Level ◽  
Night Vision ◽  
Infrared Images ◽  
Low Light ◽  
Context Enhancement

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.

Simple real-time noise removal in intensified low-light-level television images

1992 ◽  
Vol 31 (17) ◽  
pp. 3196 ◽  
Author(s):  
Yuval S. Boger ◽  
Moshe Tur
Keyword(s):  
Real Time ◽  
Light Level ◽  
Noise Removal ◽  
Low Light

Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images

2005 ◽  
Vol 51 (2) ◽  
pp. 648-653 ◽  
Author(s):  
Seong-Won Lee ◽  
V. Maik ◽  
Jihoon Jang ◽  
Jeongho Shin ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Light Level ◽  
Noise Removal ◽  
Low Light ◽  
Temporal Filter ◽  
Spatio Temporal
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