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

2019-05-28 09:27

报告人:张俊 【南昌工程娱乐】

时间:2019-05-28 15:00-16:00

地点:卫津路校区6号楼108教


报告人简介

南昌工程娱乐副教授

报告内容介绍

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.

新闻回顾

Fast Algorithms for Poisson Image Denoising using Fractional-Order Total Variation
 
张俊 副教授
 
南昌工程娱乐
 
Time15:00-16:00, May 28(Tuesday) 2019
 
VenueRoom 208, Center for Applied Mathematics
 
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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