Image reconstruction with two-dimensional piecewise polynomial convolution

This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]/spl times/[-2,2] and constrained for smooth reconstruction. The performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.