Denoising and interpolation of noisy Bayer data with adaptive cross-color filters

We propose,a novel approach,for joint denoising and interpolation of noisy Bayer-patterned data acquired,from a digital imaging sensor (e.g., CMOS, CCD). The aim is to obtain a full-resolution RGB noiseless image. The proposed technique is speciÞcally targeted to Þlter signal-dependant, e.g. Poissonian, or heteroscedastic noise, and effectively exploits the correlation between,the different color channels. The joint technique,for denoising and interpolation is based on the concept of local polynomial,approximation,(LPA) and intersection of conÞdence intervals (ICI). These directional Þlters utilize simultaneously the green, red, and blue color channels. This is achieved by a linear combination,of complementary-supported smoothing,and derivative kernels designed for the Bayer data grid. With these Þlters, the denoised and the interpolated estimates are obtained by convolutions over the Bayer data. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional Þlter. Fusing estimates from multiple directions provides the Þnal anisotropic denoised and interpolated values. The full-size RGB image,is obtained,by placing these values into the corresponding,positions in the image,grid. The efficiency of the proposed,approach,is demonstrated,by experimental,results with simulated,and real camera data. Keywords: Bayer pattern, color Þlter array interpolation, spatially adaptive denoising, sensor noise