Comparative study of some recent statistical and set-theoretic methods for image restoration

The performance is compared of a linear space-invariant (LSI) maximum a posteriori filter, an LSI reduced update Kalman filter (RUKF), an edge-adaptive RUKF, and an adaptive convex-type constraint-based restoration implemented via the method of projection onto convex sets. The finite impulse response Wiener filter is taken as a benchmark in this comparison. In image restoration, the LSI techniques are found to have some important drawbacks, such as producing ringing artifacts. As expected, the space-variant restoration methods which are adaptive to local image properties provide the best results