Corrective Term Usage in the Improvement of Gradient-Based Bayer CFA Demosaicking Algorithms

This paper presents a novel scrutiny into the role of corrective terms in gradient-based demosaicking algorithms and how they can be used to improve image reconstruction. The terms analysed are the non-zero corrective term epsilon and a new inter-plane weighting term beta. Two new techniques are proposed using these terms to highlight their contribution. Four performance metrics have been used (PSNR, CPSNR, SSIM and FSIM) for testing over four different image sets. Comparison of one the proposed algorithms is made with established current techniques and an improvement in image fidelity is observed over several image sets.