Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 3,
  • Issue 8,
  • pp. 448-450
  • (2005)

Transfer color to night vision images

Not Accessible

Your library or personal account may give you access

Abstract

Natural color appearance is the key problem of color night vision field. In this paper, the color mood of daytime color image is transferred to the monochromic night vision image. This method gives the night image a natural color appearance. For each pixel in the night vision image, the best matching pixel in the color image is found based on texture similarity measure. Entropy, energy, contrast, homogeneity, and correlation features based on co-occurrence matrix are combined as texture similarity measure to find the corresponding pixels between the two images. We use a genetic algorithm (GA) to find the optimistic weighting factors assigned to the five different features. GA is also employed in searching the matching pixels to make the color transfer algorithm faster. When the best matching pixel in the color image is found, the chromaticity values are transferred to the corresponding pixel of the night vision image. The experiment results demonstrate the efficiency of this natural color transfer technique.

© 2005 Chinese Optics Letters

PDF Article
More Like This
Tunable-liquid-crystal-filter-based low-light-level color night vision system and its image processing method

Tao Yuan, Zhenghao Han, Li Li, Weiqi Jin, Xia Wang, Hailin Wang, and Xiaofeng Bai
Appl. Opt. 58(18) 4947-4955 (2019)

Color night vision ghost imaging based on a wavelet transform

Deyang Duan, Rong Zhu, and Yunjie Xia
Opt. Lett. 46(17) 4172-4175 (2021)

Pseudo color night vision correlated imaging without an infrared focal plane array

Deyang Duan and Yunjie Xia
Opt. Express 29(4) 4978-4985 (2021)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.