Convolution filter based efficient multispectral image demosaicking for compact MSFAs

Abstract

Using the multispectral filter arrays (MSFA) and demosaicking, the low-cost multispectral imaging systems can be developed that are useful in many applications. However, multispectral image demosaicking is a challenging task because of the very sparse sampling of each spectral band present in the MSFA. The selection of MSFA is very crucial for the applicability and for the better performance of demosaicking methods. Here, we consider widely accepted and preferred MSFAs that are compact and designed using binary tree based approach and for these compact MSFAs, we propose a new efficient demosaicking method that relies on performing filtering operations and can be used for different bands size multispectral images. We also present new filters for demosaicking based on the probability of appearance of spectral bands in binary-tree based MSFAs. Detailed experiments are performed on multispectral images of two different benchmark datasets. Experimental results reveal that the proposed method has wider applicability and is efficient; it consistently outperforms the existing state-of-the-art generic multispectral image demosaicking methods in terms of different image quality metrics considered.