Abstract
The keys to multiple-algorithm fusion methods are the selection of the fusion algorithms and sequence of combination. In this paper, a new multiple-algorithm embedded fusion of infrared polarization and intensity images based on the complementary relation of the algorithms is proposed. First, indexes based on the feature similarities are applied to analyze the complementary relation. Then, in light of the complementary relation, fusion algorithms are selected and the embedded sequence is determined, and a fusion algorithm based on the energy difference degree is used to obtain the low-frequency feature fusion image, and the high-frequency feature fusion images are obtained based on the different combination of the guider filter and non-subsampled shearlet transform (NSST). Finally, the different feature fusion images are combined through two-dimensional variational mode decomposition (2D-VMD). The experiments demonstrate that the proposed method can clearly improve the fusion performance of multiple embedded infrared polarization and intensity images and generate a better image fusion.
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Funding
The authors are grateful for the financial support provided by the National Natural Science Foundation of China (grant no. 61672472), Ph.D. Project of Nanyang normal University (2019). This work was supported in part by Foundation of ExcellentYoung-Backbone Teacher of Colleges and Universities in Henan Province (grant no. 2019GGJS182) and in part by Key Scientific Research Project of Henan Colleges and Universities (grant no. 21B120001).
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Lei Zhang, Fengbao Yang 2D-VMD Embedded Fusion of Infrared Polarization and Intensity Images Using Muitiple-Algorithms Based on Their Complementary Relation. Aut. Control Comp. Sci. 56, 272–282 (2022). https://doi.org/10.3103/S0146411622030099
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DOI: https://doi.org/10.3103/S0146411622030099