Demosaicing of Captured Images using Multiscale Gradient and Heterogeneity Projection

Abstract

Color images require multiple data samples for each pixel as opposed to grayscale images for which a pixel is represented by only one data sample. For RGB format, these data samples represent red, green, blue channels. A typical digital camera captures only one of these channels at each pixel location and the other need to be estimated to generate complete color information This process is known as color filter array interpolation (CFA).The objective of the proposed research is to develop high performance, low computational complexity resolution enhancement and demosaicing algorithms. Our approach to both problems is to end creative ways to incorporate edge information into the algorithm design. However, in contrast with the usual edge directed approaches, we do not try to detect edge presence and orientation explicitly. For the image interpolation problem, we study the relationship between low resolution and high resolution pixels, and derive a general interpolation formula to be used on all pixels. This simple interpolation algorithm is able to generate sharp edges in any orientation. Additionally, we propose a gradient based directional demosaicing method that does not require setting any thresholds. We show that the performance of this algorithm can be improved by using multiscale gradients. Finally, we address the low spectral correlation Demosaicing problem by proposing a new family of hybrid colour filter array (CFA) patterns and a local algorithm that is two orders of magnitude faster than a comparable non-local solution while offering the same level of performance.