4-Band Multispectral Images Demosaicking Combining LMMSE and Adaptive Kernel Regression Methods.
Fiche publication
Date publication
octobre 2022
Journal
Journal of imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GOUTON Pierre
Tous les auteurs :
Hounsou N, Mahama ATS, Gouton P
Lien Pubmed
Résumé
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).
Mots clés
LMMSE, adaptive kernel regression, demosaicking algorithm, multispectral filter array
Référence
J Imaging. 2022 10 25;8(11):