Unrolling Multi-channel Weighted Nuclear Norm Minimization for Image Denoising

Thuy Thi Pham, Truong Thanh Nhat Mai, Chul Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We propose an unrolled deep network that integrates the flexibility of model-based algorithms and the advantages of learning-based algorithms. Specifically, based on the multi-channel optimization model for real color image denoising under the weighted nuclear norm minimization formulation, we propose an algorithm for image denoising that can learn the weights for nuclear norm from training datasets through end-to-end training. Experimental results show that the proposed algorithm achieves better performance than traditional iterative algorithms.

Original languageEnglish
Title of host publicationITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-244
Number of pages2
ISBN (Electronic)9781665485593
DOIs
StatePublished - 2022
Event37th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2022 - Phuket, Thailand
Duration: 5 Jul 20228 Jul 2022

Publication series

NameITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications

Conference

Conference37th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2022
Country/TerritoryThailand
CityPhuket
Period5/07/228/07/22

Keywords

  • Image denoising
  • unrolled optimization

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