Maximum likelihood extension for non-circulant deconvolution

Directly applying circular de-convolution to real-world blurred images usually results in boundary artifacts. Classic boundary extension techniques fail to provide likely results, in terms of a circular boundary-condition observation model. Boundary reflection gives raise to non-smooth features, especially when oblique oriented features encounter the image boundaries. Tapering the boundaries of the image support, or similar strategies (like constrained diffusion), provides smoothness on the toroidal support; however this does not guarantee consistency with the spectral properties of the blur (in particular, to its zeros). Here we propose a simple, yet effective, model-derived method for extending real-world blurred images, so that they become likely in terms of a Gaussian circular boundary-condition observation model. We achieve artifact-free results, even under highly unfavorable conditions, when other methods fail.