Natural-appearance Colorization and Enhancement for the Low-light-level Night Vision Imaging

被引:5
|
作者
Zhu Jin [1 ]
Li Li [1 ]
Jin Wei-qi [1 ,2 ]
Li Shuo [1 ]
Wang Xia [1 ]
Bai Xiao-feng [2 ]
机构
[1] Beijing Inst Technol, Sch Opt & Photon, MOE Key Lab Optoelect Imaging Technol & Syst, Beijing 100081, Peoples R China
[2] Sci & Technol Low Light Level Night Vis Lab, Xian 710059, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Low-light-level; Color night vision; Color transfer; Image enhancement; Real-time imaging;
D O I
10.3788/gzxb20184704.0410002
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The output video of the low-light-level solid-state imaging devices are always gray. For better low-light-level imaging applications, a natural-appearance colorization and enhancement method named Luminance Stretching Color Transfer (LSCT) for grayscale video images using color transfer is proposed. A two-channel natural-appearance color fusion method is refered to in the LSCT method. In order to achieve the natural-appearance colorization and enhancement, firstly, the pre-colorized image is obtained by combining the grayscale image with its negative image. Following this, an adaptive luminance stretching is performed and color of the reference image is transferred in the YUV color space. As compared with other methods based on color transfer, the LSCT method is less affected by the degree of similarity between the reference image and the original grayscale image. It means that relatively good results may be achieved for most scenes with an appropriate reference image. Thus, the LSCT method has better environmental adaptability. The comparisons reveal that the LSCT method is high efficient and its colorized results appear more natural in respect to human perception with better contrast and color harmony. Moreover, the LSCT method has been implemented in real time on hardware platforms. Therefore, it can effectively improve the effect of human observation to apply our method in the low-light-level imaging without increasing any hardware costs.
引用
收藏
页数:10
相关论文
共 20 条
  • [1] Variational Exemplar-Based Image Colorization
    Bugeau, Aurelie
    Vinh-Thong Ta
    Papadakis, Nicolas
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (01) : 298 - 307
  • [2] Charpiat G, 2008, LECT NOTES COMPUT SC, V5304, P126, DOI 10.1007/978-3-540-88690-7_10
  • [3] Semantic Colorization with Internet Images
    Chia, Alex Yong-Sang
    Zhuo, Shaojie
    Gupta, Raj Kumar
    Tai, Yu-Wing
    Cho, Siu-Yeung
    Tan, Ping
    Lin, Stephen
    [J]. ACM TRANSACTIONS ON GRAPHICS, 2011, 30 (06):
  • [4] CHUNG C H, 2009, OPTICAL ENG, V48, P55
  • [5] FU R, 2017, OPTICAL SENSING IMAG, P144
  • [6] Kekre H. B., 2008, 2008 1st International Conference on Emerging Trends in Engineering and Technology (ICETET), P82, DOI 10.1109/ICETET.2008.107
  • [7] Automatic grayscale image colorization using histogram regression
    Liu, Shiguang
    Zhang, Xiang
    [J]. PATTERN RECOGNITION LETTERS, 2012, 33 (13) : 1673 - 1681
  • [8] PHOTONIS, 2014, LOW LIGHT LEV IM SEN
  • [9] Color transfer between images
    Reinhard, E
    Ashikhmin, N
    Gooch, B
    Shirley, P
    [J]. IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2001, 21 (05) : 34 - 41
  • [10] Statistics of cone responses to natural images: implications for visual coding
    Ruderman, DL
    Cronin, TW
    Chiao, CC
    [J]. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1998, 15 (08) : 2036 - 2045