Visible and near infrared image fusion based on texture information

Yongpeng Wu; Hairong Zhang; Fan Qin; Yang Liu

doi:10.1117/12.3012281

21 December 2023 Visible and near infrared image fusion based on texture information

Yongpeng Wu, Hairong Zhang, Fan Qin, Yang Liu

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129700L (2023) https://doi.org/10.1117/12.3012281
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In this paper, we proposed a novel visible and near-infrared image fusion (VIS-NIR fusion) method based on texture filter, aiming at the problems of artifact, color distortion and noise in traditional VIS-NIR fusion methods. Firstly, the structure information of the visible (VIS) image and the near infrared (NIR) image after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, Secondly, the image noise weight is calculated by establishing a Bayesian classification model, and the noise information in the VIS information is adaptively filtered by joint bilateral filtering; finally, the fused image is acquired by color space conversion. The experimental results show that the proposed algorithm can preserve the spectral characteristics and the unique information of VIS and NIR images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongpeng Wu, Hairong Zhang, Fan Qin, and Yang Liu "Visible and near infrared image fusion based on texture information", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129700L (21 December 2023); https://doi.org/10.1117/12.3012281

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KEYWORDS

Image fusion

Near infrared

RGB color model

Tunable filters

Visible radiation

Visualization

Image filtering

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