Demosaicing: Heterogeneity-projection hard-decision adaptive interpolation using spectral-spatial correlation

Abstract

A novel heterogeneity-projection hard-decision adaptive interpolation (HPHD-AI) algorithm is proposed in this paper for color reproduction from Bayer mosaic images. The proposed algorithm aims to estimate the optimal interpolation direction and perform hard-decision interpolation, in which the decision is made before interpolation. To do so, a new heterogeneity-projection scheme based on spectral-spatial correlation is proposed to decide the best interpolation direction from the original mosaic image directly. Exploiting the proposed heterogeneity-projection scheme, a hard-decision rule can be designed easily to perform the interpolation. We have compared this technique with three recently proposed demosaicing techniques: Lu's, Gunturk's and Li's methods, by utilizing twenty-five natural images from Kodak PhotoCD. The experimental results show that HPHD-AI outperforms all of them in both PSNR values and S-CIELab Delta E-ab* measures.