NSCT domain and regional texture smoothness of Infrared and visible light image fusion

Wen Ge; Tian-chen Zhao; Peng-chong Ji

doi:10.1117/12.2245109

29 August 2016 NSCT domain and regional texture smoothness of Infrared and visible light image fusion

Wen Ge, Tian-chen Zhao, Peng-chong Ji

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331Y (2016) https://doi.org/10.1117/12.2245109
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Aiming at solving the problems of part edges loss and blurred texture information in the image fusion process of infrared light image and visible light image, an improved algorithm based on regional texture smoothness and gradation mutation degree for the infrared light and visible light image fusion is proposed. Firstly, Non-subsampled Contourlet transform(NSCT) is adopted. The multi-scale and multi-direction sparse decomposition is performed for the infrared light source image and visible light source image. Then the low-frequency component reflecting the image approximate contents and the every bandpass directional subband components reflecting the image detail characteristics are obtained respectively. The fusion method based on the gradation mutation weighted average for the low-frequency subband component is adopted, and the fusion method based on the composition of regional texture smoothness and Laplace energy sum for the high-frequency subband is adopted. Finally, the fusion image is reconstructed through the NSCT reversed transform. The experimental results show that in the condition of reserving the source image information, the fusion image clarity is raised, the detail information and brightness contrast are enhanced by this fusion method.

Citation Download Citation

Wen Ge, Tian-chen Zhao, and Peng-chong Ji "NSCT domain and regional texture smoothness of Infrared and visible light image fusion", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331Y (29 August 2016); https://doi.org/10.1117/12.2245109

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