Variational blind deconvolution of multi-channel images

The fundamental problem of de-noising and de-blurring images is addressed in this study. The great difficulty in this task is due to the ill-posedness of the problem. We suggest to analyze multi-channel images to gain robustness and to regularize it by the Polyakov action which provides an anisotropic smoothing term that use intra-channel information. Blind de-convolution is then solved by additional anisotropic smoothing term of the same type. It is shown that the Beltrami regularizer leads to better results than the Total Variation (TV) regularizer. An analytic comparison to the TV method is carried out and results on synthetic and real data are demonstrated.