Dictionary-based inverse filtering methods for blind image deconvolution

Wei Wang*, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

In this paper, we study a novel inverse filtering method by using a dictionary approach. The main idea is to combine a learned dictionary for the representation of the deconvoluted image and an inverse filter based on nonnegativity and support constraints, to deconvolute the observed image with an unknown point spread function. The advantage of this approach is that the target image can be represented with more details by learned basis in the dictionary. We also employ the alternating direction method of multipliers to solve the resulting optimization problem. Experimental results are presented to show that the performance of the proposed methods are better than other testing methods for several testing images.

Original languageEnglish
Pages (from-to)269-283
Number of pages15
JournalApplied Mathematical Modelling
Volume96
DOIs
Publication statusPublished - Aug 2021

Scopus Subject Areas

  • Modelling and Simulation
  • Applied Mathematics

User-Defined Keywords

  • Deconvolution
  • Dictionary learning
  • Inverse filtering
  • Iterative algorithm
  • Variational approach

Fingerprint

Dive into the research topics of 'Dictionary-based inverse filtering methods for blind image deconvolution'. Together they form a unique fingerprint.

Cite this