A multi-variate weighted interpolation technique with local polling for bayer CFA demosaicking

Gradient-based Spatial demosaicking techniques have gained prominence in literature for their superior reconstruction capabilities. This paper presents a novel algorithm in this class with several key contributions. It introduces variables operating at various lattice levels of the Color Filter Array (CFA) data. It also employs a Square-On-Point (SoP) neighborhood, a corrective term and localized polling to reduce reconstruction errors thus improving image perception. The proposed algorithm is compared to other contemporary methods and an appreciable improvement in performance has been noted through Matlab simulation. To provide a robust analysis, two performance metrics (CPSNR and SSIM) are used over two distinct image sets.