KEYWORDS: Associative arrays, RGB color model, Optical filters, Chemical species, Visualization, Compressed sensing, Matrices, Digital filtering, Visual compression, Digital cameras
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.