Fast fully data-driven image restoration by means of edge-preserving regularization

The fully data driven deconvolution of noisy images is a highly ill-posed problem, where the image, the blur and the noise parameters have to be simultaneously estimated from the data alone. Our approach is to exploit the information related to the image intensity edges both to improve the solution and to significantly redice the computational costs.