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Explicit Nonflat Time Evolution for PDE-Based Image Restoration

  • Seongjai Kim
  • Song-Hwa Kwon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

This article is concerned with new strategies with which explicit time-stepping procedures of PDE-based restoration models converge with a similar efficiency to implicit algorithms. Conventional explicit algorithms often require hundreds of iterations to converge. In order to overcome the difficulty and to further improve image quality, the article introduces new spatially variable constraint term and timestep size, as a method of nonflat time evolution (MONTE). It has been verified that the explicit time-stepping scheme incorporating MONTE converges in only 4-15 iterations for all restoration examples we have tested. It has proved more effective than the additive operator splitting (AOS) method in both computation time and image quality (measured in PSNR), for most cases. Since the explicit MONTE procedure is efficient in computer memory, requiring only twice the image size, it can be applied particularly for huge data sets with a great efficiency in computer memory as well.

Keywords

Image Restoration Computer Memory Constraint Parameter Spatial Scheme Explicit Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seongjai Kim
    • 1
  • Song-Hwa Kwon
    • 2
  1. 1.Department of Mathematics & StatisticsMississippi State UniversityMississippi StateUSA
  2. 2.IMAUniversity of MinnesotaMinneapolisUSA

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