Mr. Zhun Wang Profile

Zhun Wang

at Yunnan Normal Univ

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Proceedings Article | 12 January 2018 Paper

Fusion of visible and near-infrared images based on luminance estimation by weighted luminance algorithm

Zhun Wang, Feiyan Cheng, Junsheng Shi, Xiaoqiao Huang

Proceedings Volume 10620, 106200Q (2018) https://doi.org/10.1117/12.2295359

KEYWORDS: Image fusion, Near infrared, RGB color model, Denoising, Detection and tracking algorithms, Visible radiation, Image analysis, Image processing, Color imaging, Algorithm development

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In a low-light scene, capturing color images needs to be at a high-gain setting or a long-exposure setting to avoid a visible flash. However, such these setting will lead to color images with serious noise or motion blur. Several methods have been proposed to improve a noise-color image through an invisible near infrared flash image. A novel method is that the luminance component and the chroma component of the improved color image are estimated from different image sources [1]. The luminance component is estimated mainly from the NIR image via a spectral estimation, and the chroma component is estimated from the noise-color image by denoising. However, it is challenging to estimate the luminance component. This novel method to estimate the luminance component needs to generate the learning data pairs, and the processes and algorithm are complex. It is difficult to achieve practical application. In order to reduce the complexity of the luminance estimation, an improved luminance estimation algorithm is presented in this paper, which is to weight the NIR image and the denoised-color image and the weighted coefficients are based on the mean value and standard deviation of both images. Experimental results show that the same fusion effect at aspect of color fidelity and texture quality is achieved, compared the proposed method with the novel method, however, the algorithm is more simple and practical.

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