Bayesian Image Denoising with Multiple Noisy Images

Shun Kataoka () and Muneki Yasuda ()
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Shun Kataoka: Otaru University of Commerce
Muneki Yasuda: Yamagata University

The Review of Socionetwork Strategies, 2019, vol. 13, issue 2, 267-280

Abstract: 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.

Keywords: Image denoising; Discrete Markov random field; Belief propagation; EM algorithm; FFT (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:trosos:v:13:y:2019:i:2:d:10.1007_s12626-019-00043-3

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DOI: 10.1007/s12626-019-00043-3

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