A framework for wavelet-based analysis and processing of color filter array images with applications to denoising and demosaicing

This paper presents a new approach to demosaicing of spatially sampled image data observed through a color filter array, in which properties of Smith-Barnwell filterbanks are employed to exploit the correlation of color components in order to reconstruct a subsampled image. The method is shown to be amenable to wavelet-domain denoising prior to demosaicing, and a general framework for applying existing image denoising algorithms to color filter array data is also described. Results indicate that the proposed method performs on a par with the state of the art for far lower computational cost, and provides a versatile, effective, and low-complexity solution to the problem of interpolating color filter array data observed in noise.