Image identification and restoration in the subband domain

When faced with a large-sized point spread function, the expectation-maximization (EM) algorithm is very sensitive to local minima. To deal with this problem, it is proposed that EM image identification and restoration be done in the subband domain. After the image is first divided into subbands, then the EM algorithm is applied to each subband separately. In each subband the point spread function can be modeled by a reduced number of parameters and the image model can be better represented also. An adaptive subband EM method for quantization of the upper frequency subbands is introduced