期刊文献+

多尺度融合与非线性颜色传递的微光与红外图像染色 被引量:8

Coloration of the Low Light Level and Infrared Image Using Multi-scale Fusion and Nonlinear Color Transfer Technique
下载PDF
导出
摘要 Toet的夜视图像近自然色染色算法对各颜色分量使用了基于线性变换的颜色传递技术,染色结果易出现低对比度或过饱和现象,从而造成细节信息的损失。针对此问题,提出了结合多尺度融合与非线性颜色传递的微光与红外图像染色算法。选用亮度与色彩分离的YCbCr颜色空间进行染色,其中Y分量应用基于冗余小波变换的可增强感兴趣目标的融合方法构造得到,对Cb、Cr分量使用基于直方图匹配的非线性颜色传递方法实现染色。实验结果表明,本文方法可有效防止染色过程中出现的低对比度或过饱和现象,能获得纹理细节更清晰,目标更显著的染色图像。 A linear color transfer technique for each component is used in the method of near natural coloration for night vision image proposed by Toet, but the phenomenon of low-contrast or supersaturation easily appears in the results which cause the loss of detail information. Aiming at the problem, this paper proposed a novel approach for coloring the low light level and infrared image using multi-scale fusion and nonlinear color transfer technique. The YCbCr color space in which chrominance and luminance are separated is adopted for coloration process. The Y component is constructed by the fusion method which is based on redundant wavelet transform and can enhance the interesting targets; the Cb and Cr component are constructed by the nonlinear color transfer technique based on histogram matching. The experimental results show that the new method effectively prevents the phenomenon of low-contrast or supersaturation in the process of coloration, and obtain the images with clear texture and prominent targets.
出处 《红外技术》 CSCD 北大核心 2012年第12期722-728,共7页 Infrared Technology
基金 四川省苗子工程项目 编号:2011-054 中国电科集团公司CCD研发中心基础技术研究项目 西南科技大学网络融合工程实验室开放基金
关键词 红外图像 微光图像 冗余小波融合 融合规则 染色算法 直方图匹配 颜色传递 redundant wavelet fusion, fusion rule, coloration, histogram matching, color transfer
  • 相关文献

参考文献11

  • 1E. Reinhard, M. Ashikhmin, B. Gooch, et al. Color transfer between images[J]. IEEE Computer Graphics and Applications, 2001, 21(5): 34-41.
  • 2Alexander Toet. Natural colour mapping for multiband nightvision imagery[J]. Information Fusion, 2003, 4(3): 155-166.
  • 3Maarten A. Hogervorst, Alexander Toet. Method for applying daytime colors to nighttime imagery in realtime[C] //Proceedings of SPIE, 2008, 6974: 03.1-03.9.
  • 4李光鑫,徐抒岩,赵运隆,孙天宇.颜色传递技术的快速彩色图像融合[J].光学精密工程,2010,18(7):1637-1647. 被引量:13
  • 5钱小燕,韩磊,王帮峰.红外与可见光图像快速融合算法[J].计算机辅助设计与图形学学报,2011,23(7):1211-1216. 被引量:19
  • 6冈萨雷斯. 数字图像处理[M]. (第二版)北京: 电子工业出版社,2007.
  • 7杨俊,赵忠明.基于Curvelet变换的多聚焦图像融合方法[J].光电工程,2007,34(6):67-71. 被引量:20
  • 8Asmare M. H., Asirvadam V. S., Izhar L. I. Image enhancement: A composite image approach using contourlet transform[C] // Proceedings of IEEE, 2009 International Conference on Electrical Engineering and Informatics, 2009: 135.
  • 9Jean-Luc Starck, FionnMurtagh, Jalal M. Fadili. Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity[M]. Cambridge University Press, 2010.
  • 10荆绍威,杨风暴,李申燕.多源图像彩色融合效果评价研究[J].光电技术应用,2009,24(2):75-79. 被引量:1

二级参考文献54

共引文献59

同被引文献78

  • 1柏连发,张毅,顾国华,陈钱,张保民.微光图像和紫外图像分析与融合方法研究(英文)[J].红外与激光工程,2007,36(1):113-117. 被引量:10
  • 2杨静,陈昭炯.不同颜色空间中全局色彩传递算法的分析研究[J].计算机工程与应用,2007,43(25):80-82. 被引量:5
  • 3Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, 1996, 381 (6583): 607-609.
  • 4Hu J, Li S, Yang B. Remote sensing image fusion based on HIS transform and sparse representation[ C ]/ /Pattern Recognition ( CCP R ), 2010 Chinese Conference on IEEE, 2010: 1-4.
  • 5Fang Hong, Zhang Quan-Bing, Wei Sui. Image reconstruction based on improved backward optimized orthogonal matching pursuit algorithm[J]. Journal of South China University of Technology (Natural Science), 2008, 36(8): 23-27.
  • 6Aharon M, Elad M, Bruckstein A. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54( 11): 4311-4322.
  • 7Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2011, 52(2): 489-509.
  • 8Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B, 1994, 58: 267-288.
  • 9Qu Guihong, Zhang Dali, Yan Pingfan. Information measure for perfor- mance of image fusion. Electronics Letters, 2002, 38(7): 313-15.
  • 10Xydeas C S, Petrovic V. Objective image fusion performance measure[J] Electronics Letters, 2000, 36(4): 308-309.

引证文献8

二级引证文献34

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部