Hounsou Norbert, Mahama Amadou T Sanda, Gouton Pierre
Institute of Mathematics and Physical Sciences, University of Abomey-Calavi, Porto-Novo BP 613, Benin.
Science and Technology Faculty, University of Burgundy, 21078 Dijon, France.
J Imaging. 2022 Oct 25;8(11):295. doi: 10.3390/jimaging8110295.
In recent years, multispectral imaging systems are considerably expanding with a variety of multispectral demosaicking algorithms. The most crucial task is setting up an optimal multispectral demosaicking algorithm in order to reconstruct the image with less error from the raw image of a single sensor. In this paper, we presented a four-band multispectral filter array (MSFA) with the dominant blue band and a multispectral demosaicking algorithm that combines the linear minimum mean square error (LMMSE) and the adaptive kernel regression methods. To estimate the missing blue bands, we used the LMMSE algorithm and for the other spectral bands, the directional gradient method, which relies on the estimated blue bands. The adaptive kernel regression is then applied to each spectral band for their update without persistent artifacts. The experiment results demonstrate that our proposed method outperforms other existing approaches both visually and quantitatively in terms of peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM) and root mean square error (RMSE).
近年来，多光谱成像系统借助各种多光谱去马赛克算法得到了显著扩展。最关键的任务是建立一种最优的多光谱去马赛克算法，以便从单个传感器的原始图像中以较小误差重建图像。在本文中，我们提出了一种具有主导蓝色波段的四波段多光谱滤波器阵列（MSFA）以及一种将线性最小均方误差（LMMSE）和自适应核回归方法相结合的多光谱去马赛克算法。为了估计缺失的蓝色波段，我们使用了LMMSE算法，而对于其他光谱波段，则使用依赖于估计出的蓝色波段的方向梯度法。然后将自适应核回归应用于每个光谱波段以进行更新，且不会产生残留伪像。实验结果表明，我们提出的方法在峰值信噪比（PSNR）、结构相似性指数（SSIM）和均方根误差（RMSE）方面，在视觉和定量上均优于其他现有方法。
