Low-light-level image colorization based on Laws’ texture feature descriptor

Xiangyu Kong; Gangcheng Jiao; Xiaofeng Bai; Yunsheng Qian

doi:10.1117/12.2559800

17 April 2020 Low-light-level image colorization based on Laws’ texture feature descriptor

Xiangyu Kong, Gangcheng Jiao, Xiaofeng Bai, Yunsheng Qian

Proceedings Volume 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications; 114550V (2020) https://doi.org/10.1117/12.2559800
Event: Sixth Symposium on Novel Photoelectronic Detection Technology and Application, 2019, Beijing, China

Abstract

Since low-light-level images are generally grayscale images that lack color information, it is necessary to colorize these images to enhance human vision for night vision scene. In this paper, a low-light-level image colorization method is proposed based on Laws’ texture feature descriptor. Laws’ filter can analyze the texture features of the image, which are the pointers we used to find the corresponding color information for low-light-level gray images from the reference color image. The experimental results demonstrate that our colorization method can make night vision images have colors closer to natural perception and help observers better understand night vision scenes.

Citation Download Citation

Xiangyu Kong, Gangcheng Jiao, Xiaofeng Bai, and Yunsheng Qian "Low-light-level image colorization based on Laws’ texture feature descriptor", Proc. SPIE 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications, 114550V (17 April 2020); https://doi.org/10.1117/12.2559800

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KEYWORDS

Image segmentation

Night vision

Image processing

Detection and tracking algorithms

Image enhancement

Human vision and color perception

Visualization

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