3D CG image noise removal and quality assessment based on sparse dictionary learning

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

Abstract

In this paper, first, we carried out dictionary learning to process the sparse coding in advance, and then, we added six types of noise for 3D CG images. Next, we processed noise removal based on sparse coding theory and dictionary learning. Before and after image processing, we discussed improvement of image quality evaluation value eventually by measuring PSNR.

Original languageEnglish
Title of host publicationLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages225-226
Number of pages2
ISBN (Electronic)9781665418751
DOIs
Publication statusPublished - 9 Mar 2021
Event3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
Duration: 9 Mar 202111 Mar 2021

Publication series

NameLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies

Conference

Conference3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
Country/TerritoryJapan
CityNara
Period9/03/2111/03/21

Keywords

  • Dictionary learning
  • Image quality assessment
  • Noise addition
  • Noise removal
  • Sparse coding

Fingerprint

Dive into the research topics of '3D CG image noise removal and quality assessment based on sparse dictionary learning'. Together they form a unique fingerprint.

Cite this