Iterative inversion of the radon transform : Using image-adaptive wavelet constraints to improve image reconstruction

This article, briefly illustrates the image transform techniques, minimizing the number of equations, and then present for the first time new results of an iterative implementation of these techniques. The idea is that at each iteration, the image reconstructed at the previous iteration as the source of wavelet constraints for the next reconstruction is used. This is similar to iterative Wiener filtering except that the iterative filtering is spatially varying, while Wiener filtering is necessarily spatially invariant. The technique allows the advantage of spatially varying low-pass filtering to be applied iteratively. The results are that at the third iteration, the root-mean-square (rms) error is reduced by more than twice as much as in our previous results, while high-resolution image features are preserved.