Bayer and panchromatic color filter array demosaicing by sparse recovery

Abstract

The utility of Compressed Sensing (CS) for demosaicing of digital images have been explored by few recent efforts. Most recently, a Compressive Demosaicing [3] framework, based on employing a random panchromatic Color Filter Array (CFA) at the sensing stage, has provided compelling CS-based demosaicing results by visually outperforming other leading techniques. Meanwhile, it is well known that the Bayer pattern is arguably the most popular CFA used in low-cost consumer digital cameras. In this paper, we explore and compare the Bayer and random panchromatic CFA structures using a generic approach for demosaicing of images based on recent advances in the field of CS. In particular, a key objective of this work is to provide a comparative analysis between these two CFA patterns (Bayer and random panchromatic) under the general umbrella of sparse recovery, which represents the cornerstone of CS-based decoding. We demonstrate the viability of the Bayer pattern under certain CS conditions. Meanwhile, we show that a random panchromatic CFA, which meets certain incoherence constraints, can visually outperform a Bayer based sparse recovery. As illustrated in our simulation results, a panchromatic CFA is more consistent in terms of providing better visual quality when tested on a wide range of color images.