A fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform
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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.
KeywordsInfrared–visible image fusion Infrared-polarization image fusion Multi-scale top-hat transform Bright and dark feature extraction
We would like to thank the editors and the reviewers for their careful work and invaluable suggestions for helping us to improve this paper. We are also grateful to the websites www.imagefusion.org and www.vcipl.okstate.edu/otcbvs/bench/Data for providing the experiment images. This work is supported by the National Natural Science Foundation of China (Grant Nos: 61275009, 61475113).
- 16.Tan, X.Y., Chen, M., Jiang, C.S.: The small target detection base on wavelet transform and mathematical morphology. Electron. Opt. Control. 15, 25–28 (2008)Google Scholar
- 17.Barata, T., Pina, P.: Improving classification rates by modelling the clusters of training sets in features space using mathematical morphology operators. Pattern Recognit. 4, 90–93 (2002)Google Scholar
- 20.Li, Y.F., Feng, X.Y., Xu, M.W.: Infrared and visible image features enhancement and fusion using multi-scale top-hat decomposition. Infrared Laser Eng. 41(10), 2825–2832 (2012)Google Scholar