期刊文献+

基于对比度金字塔与双边滤波的非对称红外与可见光图像融合 被引量:11

Asymmetric Infrared and Visible Image Fusion Based on Contrast Pyramid and Bilateral Filtering
下载PDF
导出
摘要 为了同时保留红外图像的特征信息和可见光图像的细节信息,提出了一种基于对比度金字塔的非对称红外与可见光图像融合方法。首先,使用对比度金字塔对红外与可见光图像进行高频与低频信息分解,然后对高频部分采用绝对值取大方法进行融合,对于低频部分采用基于双边滤波的方法对红外与可见光图像进行非对称的处理;其次,使用对比度金字塔的逆变换得到融合后图像。对融合图像进行主观视觉和客观指标评价,结果表明该算法在突出目标特征信息和保留细节特征方面表现优异。 This study proposes an asymmetric infrared and visible image fusion method based on a contrast pyramid to save the feature information of infrared image and the detail information of visible image simultaneously. First, the contrast pyramid is used to decompose the high-frequency and low-frequency information of the infrared and visible images;then, the high-frequency part is fused by taking the largest absolute value, and the low-frequency part is processed differently by the method based on bilateral filtering. Second, the inverse transform of the contrast pyramid was used to obtain the fused image. Subjective visual and objective index evaluations were conducted on the fused image. The results show that the algorithm performs well in highlighting the target feature information and retaining detailed feature information.
作者 杨九章 刘炜剑 程阳 YANG Jiuzhang;LIU Weijian;CHENG Yang(Key Laboratory of Biomimetic Robots and Systems,Ministry of Education,Beijing Institute of Technology,Beijing 100081,China)
出处 《红外技术》 CSCD 北大核心 2021年第9期840-844,共5页 Infrared Technology
基金 国家自然科学基金项目(61905014) 全国博管会博士后国际交流派出计划(20190097)。
关键词 图像融合 红外图像 可见光图像 对比度金字塔 双边滤波 image fusion,infrared image,visible image contrast pyramid,bilateral filter
  • 相关文献

参考文献5

二级参考文献39

  • 1徐冠雷,王孝通,徐晓刚,朱涛.基于限邻域经验模式分解的多波段图像融合[J].红外与毫米波学报,2006,25(3):225-228. 被引量:14
  • 2Mahyari A G, Yazdi M. Panchromatic and multispectral image fu- sion based on maximization of both speclral and spatial similarities [J ]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 1-10.
  • 3Do M N, Vetterli M. The coutourlet transform: an efficient direc- tional muhiresolution image representation[ J]. IEEE Transactions on Image Processing, 2005, 14(12) : 2091 - 2106.
  • 4Yang B, Li S T, Pixel-level image fusion with simultaneous or- thogonal matching pursuit[ J]. Information Fusion, 2012, 1 ( 13 ) : 10 -19.
  • 5Yu N N, Qiu T S, Bi F. Image features extraction and fusion based on joint sparse representation[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5) :1074 -1082.
  • 6Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: theory, design, and applications [ J]. IEEE Transac- tions on Image Processing, 2006,15(10) : 3089 -3101.
  • 7Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for de- signing overcomplete dictionaries for sparse representation [ J ]. IEEE Transactions on Signal Processing, 2006, 54( 11 ) : 4311 - 4322.
  • 8Ophir B, Lustig M, Elad M. Multi-scale dictionary learning using wavelets[J]. IEEE Journal of Selected Topics in Signal Process- ing, 2011,5(5) :1014 - 1024.
  • 9Duarte M F, Sarvotham S, Baron D, et al. Distributed com- pressed sensing of jointly sparse signals [ C ]//Computer Society. Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. US: Institute of Electrical and Electronics Engineers Computer Society, 2005:1537 -1541.
  • 10Piella G, Heijmans H, A new quality metric for image fusion[ C ] //Proceedings of IEEE International Conference on Image Pro- cessing. Barcelona : IEEE ,2003 : 173 - 176.

共引文献63

同被引文献113

引证文献11

二级引证文献11

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