Seminars_raw

Fast Algorithms for Poisson Image Denoising using Fractional-Order Total Variation

2019-05-28 00:00

Speaker: Zhang Jun

unit:

Time: 2019-05-28 15:00-16:00

Venue: Room 108, Center for Applied Mathematics

starttime: 15:00

Profile:


  • Theme:

  • Fast Algorithms for Poisson Image Denoising using Fractional-Order Total Variation

  • Time:

  • 2019-05-28 15:00-16:00

  • Venue:

  • Room 108, Center for Applied Mathematics

  • Speaker:

  • Zhang Jun

Abstract

       In this talk, we present a new Poisson image denoising model based on fractional-order total variation regularization. To obtain its global optimal solution, the augmented Lagrangian method, the Chambolle’s dual algorithm and the primal-dual algorithm are introduced. Experimental results are supplied to demonstrate the effectiveness and efficiency of the proposed algorithms for solving our proposed model, with comparison to the total variation Poisson image denoising model.


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