Citation: Hounsou, N.; Mahama,
A.T.S.; Gouton, P. 4-Band
Multispectral Images Demosaicking
Combining LMMSE and Adaptive
Kernel Regression Methods. J.
Imaging 2022, 8, 295. https://
doi.org/10.3390/jimaging8110295
Academic Editor: Silvia Liberata Ullo
Received: 17 September 2022
Accepted: 16 October 2022
Published: 25 October 2022
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Article
4-Band Multispectral Images Demosaicking Combining
LMMSE and Adaptive Kernel Regression Methods
Norbert Hounsou
1,
* , Amadou T. Sanda Mahama
1
and Pierre Gouton
2
1
Institute of Mathematics and Physical Sciences, University of Abomey-Calavi, Porto-Novo BP 613, Benin
2
Science and Technology Faculty, University of Burgundy, 21078 Dijon, France
* Correspondence: norbert.hounsou@imsp-uac.org; Tel.: +229-97-50-55-56
Abstract:
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).
Keywords: demosaicking algorithm; multispectral filter array; LMMSE; adaptive kernel regression
1. Introduction
Digital color cameras generally sensitive to three bands of the visible electromagnetic
spectrum are used to capture digital color images representing the reflectance of the
observed object. Nowadays, technological advancement has made it possible to overcome
this three-band limitation with the development of multispectral digital cameras to acquire
multispectral images with more than three spectral bands per pixel. There are several types
of multispectral image acquisition systems including single-sensor one-shot cameras which
are equipped with a multispectral filter mosaic. However, in the raw image from the sensor,
each pixel is characterized by a single available spectral band. We will have to reconstruct
the missing spectral bands by the demosaicking method. The reconstruction performance
depends on the optimal choice of MSFA and the multispectral demosaicking algorithm.
Several MSFA patterns are proposed in the literature. To our knowledge, Miao et al. [
1
]
are the first to propose a generic MSFA model from a binary tree by recursively separating
the checkerboard pattern based on a tree decomposition which defines the number of
spectral bands and the probability of occurrence of each band. Aggarwal et al. [
2
] mean-
while implemented two MSFA patterns, one random and the other uniform, which can be
generalized to any number of bands. In [
3
], Monno et al. proposed a five-band MSFA based
on the dominant G band requirement, which is used by Jaiswal et al. in their multispectral
demosaicking algorithm [
4
]. To overcome the difficulties in combining spectral resolution
and spatial correlation, Mihoubi et al. proposed a 16-band MSFA without a dominant
spectral band [
5
]. Recently, Bangyong et al. designed a uniform four-band MSFA pattern [
6
]
with the same probability of occurrence for each band and a nine-band MSFA pattern [
7
] in
which one band is dominant and the other eight have the same probability of occurrence
arranged in a 4 × 4 mosaic.
J. Imaging 2022, 8, 295. https://doi.org/10.3390/jimaging8110295 https://www.mdpi.com/journal/jimaging
