Recursive blind image deconvolution via dispersion minimization

This paper presents a method that uses an autoregressive filter for deblurring noisy blurred images blindly. The approach has several important advantages over using a finite impulse response filter. The optimum support of the adaptive autoregressive filter is the same as the support of the blur, and so the truncation error introduced by the finite support of the adaptive finite impulse response filter can be made arbitrarily small. Furthermore, the method can also be used for blur identification. In addition, the resulting improvement in signal-to-noise ratios are higher and convergence of the adaptive filter coefficients is faster for a given blur. First, an autoregressive method is naively derived via a gradient method to minimize the dispersion. This leads to a recursion within a recursion which is computationally complex. Next, a simplification of the method is proposed. Finally, simulations demonstrate the performance of the simplified method.