Regularized iterative image restoration based on an iteratively updated convex smoothing functional

Moon Gi Kang, Aggelos K. Katsaggelos

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


The determination of the regularization parameter is an important issue in regularized image restoration, since it controls the trade-off between fidelity to the data and smoothness of the solution. A number of approaches have been developed in determining this parameter. In this paper, we propose the use of a regularization functional instead of a constant regularization parameter. The properties such a regularization functional should satisfy are investigated, and two specific forms of it are proposed. An iterative algorithm is proposed for obtaining a restored image. The regularization functional is defined in terms of the restored image at each iteration step, therefore allowing for the simultaneous determination of its value and the restoration of the degraded image. Both proposed iteration adaptive regularization functionals are shown to result in a smoothing functional with a global minimum, so that its iterative optimization does not depend on the initial conditions. The convergence of the algorithm is established and experimental results are shown.

Original languageEnglish
Pages (from-to)1364-1375
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1993
EventVisual Communications and Image Processing 1993 - Cambridge, MA, United States
Duration: 1993 Nov 71993 Nov 7

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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