Improvement of Space-Invariant Image Deblurring by Preconditioned Landweber Iterations

The Landweber method is a simple and flexible iterative regularization algorithm, whose projected variant provides nonnegative image reconstructions. Since the method is usually very slow, we apply circulant preconditioners, exploiting the shift invariance of many deblurring problems, in order to accelerate the convergence. This way reasonable reconstructions can be obtained within few iterations: the method becomes competitive and more robust than other approaches that, although faster, sometimes lead to lower accuracy. Some theoretical analysis of convergence is given, together with numerical validations.