Hardware-friendly universal demosaick using non-iterative map reconstruction

Non-Bayer color filter array (CFA) sensors have recently drawn attention due to their superior compression of spectral energy, ability to deliver improved signal-to-noise ratio, or ability to provide high dynamic range (HDR) imaging. Demosaicking methods that perform color interpolation of Bayer CFA data have been widely investigated. However, a bottleneck to the adaption of emerging non-Bayer CFA sensors is the unavailability of efficient color-interpolation algorithms that can demosaick the new patterns. Designing a new demosaick algorithm for every proposed CFA pattern is a challenge. In this paper, we propose a hardware-friendly universal demosaick algorithm based on maximum a-posteriori (MAP) estimation that can be configured to demosaick raw images captured using a variety of CFA sensors. The forward process of mosaicking is modeled as a linear operation. We then use quadratic data-fitting and image prior terms in a MAP framework and pre-compute the inverse matrix for recovering the full RGB image from CFA observations for a given pattern. The pre-computed inverse is later used in real-time application to demosaick the given CFA pattern. The inverse matrix is observed to have a Toeplitz-like structure, allowing for hardware-efficient implementation of the algorithm. We use a set of 24 Kodak color images to evaluate the quality of our demosaick algorithm on three different CFA patterns. The PSNR values of the reconstructed full-channel RGB images from CFA samples are reported in the paper.