Joint deblurring and demosaicing of Poissonian Bayer-data based on local adaptivity

We present a novel technique for joint deblurring and demosaicing of noisy Poissonian Bayer data (e.g., data acquired by a digital CMOS or CCD imaging sensor). The technique incorporates the regularized inverse and the Wiener inverse with adaptive filtering based on the concept of cross-color local polynomial approximation (LPA) and intersection of confidence intervals (ICI). The directional filters designed by LPA utilize simultaneously the green, red, and blue color components. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Simulation experiments demonstrate the efficiency of the proposed technique with respect to the conventional approach where deconvolution and demosaicing are computed independently.