New algorithms of two-dimensional blind deconvolution

We propose a few new algorithms of two-dimensional blind deconvolution. It is known that discretization of a continuous deconvolution problem can alleviate its ill-posedness. This philosophy can be embodied by using the aperiodic convolution model. We illustrate a few gradient algorithms of blind deconvolution using the aperiodic model and show that the main calculations can be accomplished efficiently by means of the discrete Fourier transform (DFT) technique. We present an increment Wiener filter that can further improve the solution of the frequency domain deconvolution. The increment Wiener filter has an iterative form and therefore the object domain constraints can be efficiently incorporated. We propose a multiframe combination scheme of blind deconvolution that can stabilize the solution of the iterative algorithms and suggest two useful techniques: the numerical level adjustment and the updating of the support constraints. We propose that the combination of the Wiener filter, the increment Wiener filter, and the new gradient algorithm is a reasonable choice for practical blind deconvolution applications.