Regularized Blind Deconvolution

Image restoration involves the removal or minimization of degradation (blur, clutter, noise, etc.) in an image using a priori knowledge about the degradation phenomena. Blind restoration is the process of estimating both the true image and the blur from the degraded image characteristics, using only partial information about degradation sources and the imaging system. Our main interest concerns optical image enhancement , where the degradation involves a convolution process. When an otherwise collimated, coherent beam of light encounters a turbulent ow eld that includes density uctuations, its optical wavefront becomes aberrated causing the beam to be degraded. Only partial a priori knowledge about the degradation phenomena in aero-optics is generally known, so here the use of blind deconvolution methods is essential. In this paper we provide a method to incorporate truncated eigenvalue and total variation regularization into a nonlinear recursive inverse lter blind deconvolution scheme rst proposed by Kundur and Hatzinakos. We call our approach the nonnegativity and support regularized recur-sive inverse lter (NSR-RIF) algorithm. Simulation tests are reported on optical imaging problems.