A new approach to constrained total least squares image restoration

Recently there has been a growing interest and progress in using total least squares (TLS) methods for solving blind deconvolution problems arising in image restoration. Here, the true image is to be estimated using only partial information about the blurring operator, or point spread function (PSF), which is subject to error and noise. In this paper, we present a new iterative, regularized, and constrained TLS image restoration algorithm. Neumann boundary conditions are used to reduce the boundary artifacts that normally occur in restoration processes. Preliminary numerical tests are reported on some simulated optical imaging problems in order to illustrate the effectiveness of the approach, as well as the fast convergence of our iterative scheme.