Regularized Iterative Blind Deconvolutionusing Recursive Inverse

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 the true image 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 recursive inverse lter (NSR-RIF) algorithm. Inverse lters are easier to implement and avoid certain inversion procedures associated with direct ltering methods, thus reducing the computational complexity. Simulation tests are reported on optical imaging problems.