Tunable-liquid-crystal-filter-based low-light-level color night vision system and its image processing method

Yuan, Tao; Han, Zhenghao; Li, Li; Jin, Weiqi; Wang, Xia; Wang, Hailin; Bai, Xiaofeng

doi:10.1364/ao.58.004947

Cited by 4 publications

(2 citation statements)

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“…The LCTF-ICMOS imaging system was improved on the basis of the results in [ 6 ]. The system was mainly composed of an objective lens, the LCTF and its controller, the GaAsP third-generation image intensifier, the relay lens, a low-light-level CMOS, and a PC.…”

Section: Methodsmentioning

confidence: 99%

“…Generally, the ICMOS/ICCD has better imaging performance compared with low-light-level imaging devices such as sCMOS and EMCCD. With the development of the sensitivity of third-generation image intensifiers, it is of great significance to expand their application to low-light-level color imaging through the modulation of filtering elements such as a filter wheel [ 2 , 3 , 4 ] and a liquid-crystal tunable filter (LCTF) [ 5 , 6 ], whereby it may be possible for low-light-level color imaging techniques to expand their extreme working illuminant level to 10 −3 lx or even 10 −4 lx.…”

Section: Introductionmentioning

confidence: 99%

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Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS

Han

Jin

et al. 2021

Sensors

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Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%