Fast Fully Data-Driven Image Restoration by means of Edge-Preserving Regularization

Abstract

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 reduce the computational costs. To detect reliable intensity edges, the image is modeled through a coupled Markov Random Field with an explicit, binary and constrained line process. Following a fully Bayesian approach, the solution should be given by the joint maximization of a distribution of the image field, the data, the blur and model parameters. A first, significant reduction in computational complexity is obtained by decomposing this joint maximization into a sequence of Maximum a posteriori and/or Maximum Likelihood estimations, to be performed alternately and iteratively. The presence of an explicit and binary line field is then exploited to reduce the computational cost of the usually very expensive model parameter estimation step. On this basis, we derive efficient and fast algorithms along with procedures which are feasible and effective for real-time applications, where the real-time requirements are not too strict. Indeed, the structure of these algorithms are intrinsically parallel, and thus suitable for implementation on high-performance machines, or on specialized hardware and allows the computation time to be greatly reduced. The experimental results show that the method allows one to obtain good blur estimates even in the presence of noise, without any need for smoothness assumptions on the blur coefficients, which would polarize the solution towards often unrealistic uniform blurs.