Bayesian Image Denoising with Multiple Noisy Images

• Kataoka, Shun

• Yasuda, Muneki

Abstract

In this paper, we propose a fast image denoising method based on discrete Markov random fields and the fast Fourier transform. The purpose of the image denoising is to infer the original noiseless image from a noise corrupted image. We consider the case where several noisy images are available for inferring the original image and the Bayesian approach is adopted to create the posterior probability distribution of the denoised image. In the proposed method, the estimation of the denoised image is achieved using belief propagation and an expectation-maximization algorithm. We numerically verified the performance of the proposed method using several standard images.

Journal

• The Review of Socionetwork Strategies

  The Review of Socionetwork Strategies 13 (2), 267-280, 2019

  Springer

Keywords

• Image denoising
• Discrete Markov random field
• Belief propagation
• EM algorithm
• FFT
• 数学

Details

• CRID: 1050845763835201280
• NII Article ID: 120006767844
• ISSN: 25233173
• ISSN: 18673236
• DOI: 10.1007/s12626-019-00043-3
• Web Site: http://hdl.handle.net/10252/00005944
• Web Site: http://link.springer.com/content/pdf/10.1007/s12626-019-00043-3.pdf
• Web Site: http://link.springer.com/article/10.1007/s12626-019-00043-3/fulltext.html
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN