Fast image reconstruction algorithms combining half-quadratic regularization and preconditioning

M. Nikolova, M. Ng

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

22 Citations (Scopus)

Abstract

In this paper, we focus on image deconvolution and image reconstruction problems where a sought image is recovered from degraded observed data. The solution is defined to be the minimizer of an objective function combining a data-fidelity term and a edge-preserving, convex regularization term. Our objective is to speed up the calculation of the solution in a wide range of situations. To this end, we propose a method applying pertinent preconditioning to an adapted half-quadratic equivalent form of the objective function. The optimal solution is then found using an alternating minimization (AM) scheme. We focus specifically on Huber regularization. We exhibit the possibility get very fast calculations while preserving the edges in the solution. Preliminary numerical results are reported to illustrate the effectiveness of our method.

Original languageEnglish
Title of host publicationProceedings of 2001 International Conference on Image Processing, ICIP 2001
PublisherIEEE
Pages277-280
Number of pages4
ISBN (Print)0780367251
DOIs
Publication statusPublished - 7 Aug 2001
Event2001 IEEE International Conference on Image Processing, ICIP 2001 - Thessaloniki, Greece
Duration: 7 Oct 200110 Oct 2001
https://ieeexplore.ieee.org/xpl/conhome/7594/proceeding

Publication series

NameProceedings of International Conference on Image Processing

Conference

Conference2001 IEEE International Conference on Image Processing, ICIP 2001
Country/TerritoryGreece
CityThessaloniki
Period7/10/0110/10/01
Internet address

Scopus Subject Areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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