Degradation reduction in optics imagery using Toeplitz structure

This paper is concerned with improvement in optical image quality by image restoration. Image restoration is an ill-posed inverse problem which involves the removal or minimization of degradations caused by noise and blur in an image, resulting from, in this case, imaging through a medium. Our work here concerns the use of the underlying Toeplitz structure of such problems, and associated techniques for accelerating the convergence of iterative image restoration computations. Denoising methods, including total variation minimization, followed by segmentation-based preconditioning methods for minimum residual conjugate gradient iterations, are investigated. Regularization is accomplished by segmenting the image into (smooth) segments and varying the preconditioners across the segments. By taking advantage of the Toeplitz structure, our algorithms can be implemented with computational complexity of onlyO (ln ² logn), wheren ² is the number of pixels in the image andl is the number of segments used. Also, parallelization is straightforward. Numerical tests are reported for atmospheric imaging problems, including the case of spatially varying blur. Research supported in part by a National Science Foundation Postdoctoral Research Fellowship. Research sponsored by the U.S. Air Force Office of Scientific Research under grant F49620-97-1-1039. Research sponsored by the U.S. Air Force Office of Scientific Research under grant F49620-97-1-0139, and by the National Science Foundation under grant CCR-96-23356. Research sponsored by the National Science Foundation under grant CCR-96-23356.