A fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform

被引:0
作者
Pan Zhu
Zhanhua Huang
机构
[1] Ministry of Education,Key Laboratory of Opto
[2] Tianjin University,electronic Information Technology
来源
Optical Review | 2017年 / 24卷
关键词
Infrared–visible image fusion; Infrared-polarization image fusion; Multi-scale top-hat transform; Bright and dark feature extraction;
D O I
暂无
中图分类号
学科分类号
摘要
This paper presents a fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform which can effectively extract the feature information and detail information of source images. Firstly, the multi-scale bright (dark) feature regions of source images at different scale levels are respectively extracted by multi-scale center-surround top-hat transform. Secondly, the bright (dark) feature regions at different scale levels are refined for eliminating the redundancies by spatial scale. Thirdly, the refined bright (dark) feature regions from different scales are combined into the fused bright (dark) feature regions through adding. Then, a base image is calculated by performing dilation and erosion on the source images with the largest scale outer structure element. Finally, the fusion image is obtained by importing the fused bright and dark features into the base image with a reasonable strategy. Experimental results indicate that the proposed fusion method can obtain state-of-the-art performance in both aspects of objective assessment and subjective visual quality.
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页码:370 / 382
页数:12
相关论文
共 74 条
  • [1] Kong W(2013)Technique for image fusion between gray-scale visual light and infrared images based on NSST and improved RF Optik 124 6423-6431
  • [2] Yang L(2012)Infrared and visible image fusion using fuzzy logic and population-based optimization Appl. Soft. Comput. 12 1041-1054
  • [3] Jamal S(2006)Review of passive imaging polarimetry for remote sensing applications Appl. Opt. 45 5453-5469
  • [4] Karim F(1983)The laplacian pyramid as compact image code IEEE Trans. Commun. 31 532-540
  • [5] Scott TJ(2012)Maximum local energy: an effective approach for multisensory image fusion in beyond wavelet transform domain Comput. Math. Appl. 64 996-1003
  • [6] Goldstein DL(2013)Tunable-Q contourlet-based multi-sensor image fusion Signal Process. 93 1879-1891
  • [7] Chenault DB(2011)Biological image fusion using a NSCT based variable-weight method Inf. Fusion. 12 85-92
  • [8] Shaw JA(2009)Multifocus image fusion using the nonsubsampled contourlet transform Signal Process. 89 1334-1346
  • [9] Burt P(2011)Performance comparison of different multi-resolution transforms for image fusion Inf. Fusion. 12 74-84
  • [10] Adelson E(2004)Morphological image sequence processing Comput. Vis. Sci. 6 197-209