A fast denoising algorithm for X-ray images with variance stabilizing transform

Nguyen Hoang Hai, Dang N.H. Thanh, Nguyen Ngoc Hien, Chinthaka Premachandra, V. B.Surya Prasath

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We propose a fast denoising algorithm for X-Ray images with variance stabilizing transformations. The variance stabilizing transformations are used to transform Poisson noisy images to Gaussian noisy images. Therefore, we can utilize advantages of the fast denoising algorithm based on the alternative direction method of multipliers. In experiments, we evaluate denoising quality by the Peak signal-to-noise ratio and the Structure Similarity metrics. Comparing results show that our method outperforms other similar denoising methods.

Original languageEnglish
Title of host publicationProceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019
EditorsJosiane Mothe, Le Hoang Son, Nguyen Tran Quoc Vinh
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130033
DOIs
Publication statusPublished - 2019 Oct
Event11th International Conference on Knowledge and Systems Engineering, KSE 2019 - Da Nang, Viet Nam
Duration: 2019 Oct 242019 Oct 26

Publication series

NameProceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019

Conference

Conference11th International Conference on Knowledge and Systems Engineering, KSE 2019
Country/TerritoryViet Nam
CityDa Nang
Period19/10/2419/10/26

Keywords

  • Image Denoising
  • Medical Image Processing
  • Poisson Noise
  • ROF model
  • Variance Stabilizing Transformations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'A fast denoising algorithm for X-ray images with variance stabilizing transform'. Together they form a unique fingerprint.

Cite this