Filterbank-based universal demosaicking

Abstract

Recent advances in spatio-spectral sampling and panchromatic pixels have contributed to increased spatial resolution and enhanced noise performance. As such, it is necessary to consider the universality of demosaicking design principles—instead of CFA-specific optimization for signal recovery. In this article, we introduce a new universal demosaicking method that draws from the lessons learned in Bayer demosaicking designs, but can be applied to arbitrary array patterns. We recast the data-dependence of Bayer demosaicking as a parsimonious reconstruction of the underlying image signal that is inherently sparse in some representation. Using properties of filterbanks, we generalize this principle to yield a nonlinear recovery method that is consistent with the state-of-the-art Bayer demosaicking methods.