title: Deblurring random time-varying blur.
abstract: The problem of restoring a constant image distorted by a system of random time-varying point-spread functions is studied. The restoration is based on a finite number of images that are observed in a finite period of time. Two features distinguish this problem. The first is that of the signal-noise dependency, and the second is the availability of large amounts of data. The Wiener criterion approach is used to solve the signal-noise-dependency problem. The problem of data size is also alleviated. For the case of time-space separability, a Karhunen-Loève transformation is used to reduce the computations to the size of a single-frame problem. For the case in which the noise is stationary in time and in space, a solution based on the direct form of the Wiener filter is presented. The amount of computations here is reduced considerably by the use of fast Fourier transforms and circulant matrix approximations whenever they are valid.
authors.affiliation: Department of Electrical Engineering, University of British Columbia, Vancouver, Canada.
authors.forename: L
authors.initials: L
authors.lastname: Guan
authors.forename: R K
authors.initials: RK
authors.lastname: Ward
mesh_terms: D000465:Algorithms; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D055095:Optics and Photonics