Color image denoising and blind deconvolution using the Beltrami operator

A new method for the recovery of noisy and blurred color images is presented. The image is reconstructed and the blurring kernel is approximated, under the assumption of linearity and spatial invariance of the blurring kernel. It is done by combining the Beltrami operator, which was introduced as a general framework for low-level vision, with the scheme of the blind deconvolution, which was introduced for the recovery of blurred and noisy gray value images. Consequently, image and kernel edges are preserved due to the adaptive smoothing feature of this operator. The color channels are coupled by a Riemannian structure, which is defined on the color image. The functional minimization scheme is presented and results of applying it in the recovery of blurred and noisy color images are illustrated.