Selection of Varying Spatially Adaptive Regularization Parameter for Image Deconvolution

The deconvolution in image processing is an inverse illposed problem which necessitates a trade-off between delity to data and smoothness of a solution adjusted by a regularization parameter. In this paper we propose two techniques for selection of a varying regularization parameter minimizing the mean squared error for every pixel of the image. The rst algorithm uses the estimate of the squared point-wise bias of the regularized inverse. The second algorithm is based on direct multiple statistical hypothesis testing for the estimates calculated with different regularization parameters. The simulation results on images illustrate the ef ciency of the proposed technique.