红外技术, 2018, 40 (5): 431, 网络出版: 2018-08-04   

基于BP神经网络的自然感彩色融合算法

Natural Color Fusion Algorithm Based on BP Neural Network
作者单位
陆军装甲兵学院 控制工程系,北京 100072
摘要
红外与可见光图像的自然感彩色融合能够显著提高人眼视觉的情景感知和目标探测能力。基于样本的融合算法是一种快速有效、实时性强的自然感彩色融合算法。针对已有算法构建颜色查找表时存在的样本数据冲突和查找表不完整两个难题,提出一种基于BP 神经网络的自然感彩色融合算法。算法采用BP 神经网络对图像样本的二维灰度向量(g1, g2)和三维色彩向量(R, G, B)进行非线性拟合,从而获得灰度与色彩间的映射关系f(g1, g2)→(R, G, B),并由此构建完整的颜色查找表。融合时,由输入的双波段图像的灰度g1,g2 和颜色查找表得到彩色融合图像。实验结果表明,本文融合图像颜色自然,景物易于分辨,在清晰度、彩色性、映射准确性方面已经达到甚至优于Toet 算法的图像融合效果。
Abstract
The natural color fusion of infrared images and visible images can significantly improve the situation perception and target detection of human vision. Sample-based color fusion is a fast, effective, and real-time natural color fusion algorithm. In view of the problems of the existing algorithm in terms of the color look-up table construction, i.e., sample data collision and look-up table incompleteness, this paper proposes a new natural color fusion algorithm based on the BP neural network. In the algorithm, the mapping f(g1, g2)→(R, G, B) between grayscale and color is obtained by using the BP neural network to nonlinearly fit between the two-dimensional grayscale vector (g1, g2) and the three-dimensional color vector (R, G, B) of the image simples. Subsequently, the color look-up table is constructed based on the mapping. During color fusing, the fused image is obtained by the color look-up table and the input grayscale g1,g2 of dual-band images. The experiments show that the fused images based on the proposed algorithm have natural colors and are easily distinguishable objects. The fusion results obtained by the proposed algorithm are almost as good as or even better than the fusion results by Toet’s method in terms of definition, colorfulness, and mapping accuracy.
参考文献

[1] ] LI S, KANG X, FANG L, et al. Pixel-level image fusion: A survey of the state of the art[J]. Information Fusion, 2017, 33: 100-112.

[2] 陈清江,张彦博,柴昱洲,等.有限离散剪切波域的红外可见光图像融合[J].中国光学,2016,9(5):523-531.

    CHEN Qingjiang, ZHANG Yanbo, CHAI Yuzhou, et al. Fusion of infrared and visible images based on finite discrete shearlet domain[J]. Chinese Optics, 2016, 9(5): 523-531.

[3] Mcdaniel R V, Ockman N, Scribner D A, et al. Image fusion for tactical applications[C]//Proceedings of SPIE-The International Society for Optical Engineering, 1999, 3436: 685-695.

[4] Toet A, Walraven J. New false color mapping for image fusion[J]. Optical Engineering, 1996, 35(3): 650-658.

[5] Waxman A M, Gove A N, Fay D A, et al. Color night vision: opponent processing in the fusion of visible and IR imagery[J]. Neural Networks, 1997, 10(1):1-6.

[6] Geoffrey W. Stuart, Philip K. Hughes. Towards Understanding the Role of Colour Information in Scene Perception using Night Vision Device[R]. Australia: DSTO Defence Science and Technology Organisation, 2009.

[7] Toet A. Natural colour mapping for multiband nightvision imagery[J]. Information fusion, 2003, 4(3): 155-166.

[8] ZHENG Y, Essock E A. A local-coloring method for night-vision colorization utilizing image analysis and fusion[J]. Information Fusion, 2008, 9(2): 186-199.

[9] YANG B, SUN F, LI S. Region-Based Color Fusion Method for Visible and IR Image Sequences[C]// Pattern Recognition, 2008. CCPR'08. Chinese Conference on. IEEE, 2008: 1-6.

[10] 赵源萌,王岭雪,金伟其,等.基于区域直方图统计的灰度图像色彩传递方法[J].北京理工大学学报,2012,32(3):322-326.

    ZHAO Yuanmeng, WANG Lingxue, JIN Weiqi, et al. A Color Transfer Method for Colorization of Grayscale Image Based on Region Histogram Statistics[J]. Transactions of Beijing Institute of Technology, 2012, 32(3): 322-326.

[11] 薛模根,周浦城,刘存超.夜视图像局部颜色传递算法[J].红外与激光工程,2015,44(2):781-785.

    XUE Mogen, ZHOU Pucheng, LIU Cunchao. A novel local color transfer method for night vision image[J]. Infrared and Laser Engineering, 2015, 44(2): 781-785.

[12] Hogervorst M A, Toet A. Method for applying daytime colors to nighttime imagery in realtime[C]//Proc. SPIE, 2008, 6974: 697403.

[13] Hogervorst M A, Toet A. Fast natural color mapping for night-time imagery[J]. Information Fusion, 2010, 11(2): 69-77.

[14] Hogervorst M A, Toet A. Improved colour matching technique for fused nighttime imagery with daytime colours[C]// Proc. SPIE, 2016, 9987: 99870J.

[15] Hogervorst M A, Toet A. Improved Color Mapping Methods for Multiband Nighttime[J] Image Fusion. 2017, 3(36): 1-25.

[16] 王岭雪,金伟其,赵源萌,等.基于颜色查找表的双波段视频快速自然感彩色融合方法:101867685[P].2010-10-20.

    WANG Lingxue, JIN Weiqi, Zhao Yuanmeng, et al. Fast natural color fusion method of dual-band video based on color look-up table: 101867685[P]. 2010-10-20.

[17] 金真.基于多维信息颜色查找表的计算彩色成像[D].北京:北京理工大学,2015.

    JIN Zhen. Computational Color Imaging based on the Color Look-up Table of Multidimensional Information [D]. Beijing: Beijing Institute of Technology, 2015.

[18] 姜曼.基于二维颜色查找表的双波段彩色图像融合算法研究[D].北京:北京理工大学,2015.

    JIANG Man. Study on double-band color image fusion algorithm based on 2-D color look-up table[D]. Beijing: Beijing Institute of Technology, 2015.

[19] McClelland J L, Rumelhart D E. PDP Research Group. Parallel distributed processing[M]. Cambridge, MA: MIT press, 1987.

[20] Hecht-Nielsen R. Theory of the backpropagation neural network[J]. Neural Networks, 1988, 1(s1): 445-448.

[21] Toet A. TNO Image fusion dataset [DB/OL]. [2014-4-26]. http://dx. doi.org/10.6084/m9.figshare.1008029.

[22] Toet A, Hogervorst M A, Pinkus A R.. The TRICLOBS Dynamic Multiband Image Dataset [DB/OL]. [2016-10-8]. https://figshare.com/ articles/The_TRICLOBS_Dynamic_Multiband_Image_Dataset/3206887.

[23] YUAN Y, ZHANG J, CHANG B. Objective quality evaluation of visible and infrared color fusion image[J]. Optical Engineering, 2011, 50(3): 103-108.

[24] 高绍姝,金伟其,王岭雪,等.基于颜色协调性的典型场景彩色融合图像颜色质量评价[J].北京理工大学学报,2012,32(10):1054-1060.

    GAO Shaoshu, JIN Weiqi, WANG Lingxue, et al. Color-Quality Assessment for Color Fusion Images of Typical Scenes Based on Color Harmony[J]. Transactions of Beijing Institute of Technology, 2012, 32(10): 1054-1060.

何炳阳, 张智诠, 李强, 谢志宏. 基于BP神经网络的自然感彩色融合算法[J]. 红外技术, 2018, 40(5): 431. HE Bingyang, ZHANG Zhiquan, LI Qiang, XIE Zhihong. Natural Color Fusion Algorithm Based on BP Neural Network[J]. Infrared Technology, 2018, 40(5): 431.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!