The Lane-Bates method of blind deconvolution makes it possible to analytically recover the original image without prior knowledge of blurs convolved in a given image. The method utilizes the zeros of the z-transform of the given image. Its implementation, however, requires highly nontrivial analysis of the zeros. We have developed a novel scheme that considerably simplifies the analysis of the zeros. We have developed two versions of the scheme, i.e., determinant conditions (DCs) for the zeros of blurs and a search algorithm (SA) of blur images. The DCs consist of two forms, i.e., a derivative form and a multi-point form. The derivative form is given as a determinant form of conditions on derivatives of the zeros of assumed blurs that can be evaluated by using zeros of the z-transform of the given image. On the other hand, the multi-point form is given as a determinant form of conditions on the zeros of assumed blurs that are evaluated at multiple points in z space. The scheme is particularly powerful when the blurs have multiple structures as we illustrate. The SA is given as a form of simultaneous equations for blur elements of an assumed blur. The method is powerful when we try to find a single blur. This method is robust for compressed gray-scale images. These methods have been experimentally tested with model blurred images and shown to be powerful. In this report we illustrate how they are useful for the Lane-Bates blind deconvolution
