A visible polarization image fusion algorithm based on NSST transform

Zhaozhen Jiang; Yusheng Han; Fei Ye; Shuaijun Ren; Hao Zhai; Zhenghao Hu

doi:10.1117/12.2579549

5 November 2020 A visible polarization image fusion algorithm based on NSST transform

Zhaozhen Jiang, Yusheng Han, Fei Ye, Shuaijun Ren, Hao Zhai, Zhenghao Hu

Proceedings Volume 11567, AOPC 2020: Optical Sensing and Imaging Technology; 115671V (2020) https://doi.org/10.1117/12.2579549
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Image information for single polarization parameters is weak, low contrast and the common visible light intensity image detail fuzzy problems, in order to further improve the target detection of polarization imaging detection system identification capability, put forward a kind of based on the sampling of shear wave transformation under visible light image fusion algorithms, intensity and polarization parameters can effectively improve the identification of targets in complex background. Polarization degree of the image and visible light intensity image using the sampling shear wave transformation under the decomposed high frequency subband and low-frequency subband, then low frequency subband image fusion rules design based on region distance energy weighted algorithm, and the high frequency subband image fusion rule is designed to combine guide take large filtering area of energy, will eventually high low frequency subband image by the NSST finally fused image is obtained by inverse transformation refactoring. By comparing the fusion results of this algorithm with those of other methods that adopt the same decomposition transformation method but choose different fusion rules, the experiment proves that this algorithm not only has the best visual effect, but also has the advantage in the objective evaluation index value.

Citation Download Citation

Zhaozhen Jiang, Yusheng Han, Fei Ye, Shuaijun Ren, Hao Zhai, and Zhenghao Hu "A visible polarization image fusion algorithm based on NSST transform", Proc. SPIE 11567, AOPC 2020: Optical Sensing and Imaging Technology, 115671V (5 November 2020); https://doi.org/10.1117/12.2579549

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