On the Numerical Distortion-Mutual Information Function of Image Denoising with Deep Convolutional Networks

Conference proceedings article

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Publication Details

Author list: Kumwilaisak W., Piriyatharawet T., Lasang P., Thatphithakkul N.

Publisher: Hindawi

Publication year: 2020

Start page: 474

End page: 477

Number of pages: 4

ISBN: 9781728164861

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091840092&doi=10.1109%2fECTI-CON49241.2020.9158122&partnerID=40&md5=82c2df3c19959b47b9508e0128834338

Languages: English-Great Britain (EN-GB)

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Abstract

This paper presents a new perspective on the image denoising problem. The architecture of image denoising algorithm used in this paper is the Deep Convolutional Neural Networks (DCNN). Designing DCNN models relates tuning hyperparameters such as a number of layers of DCNN. To choose suitable hyperparameters, it would has an advantage to examine the best achievable image denoising performance under different a number of DCNN layers. Moreover realizing how close the image denoising algorithm performance to the optimal result allows us to design the efficient image denoising algorithm. The Blahut-Arimoto algorithm is used to derive numerically distortion-mutual information function of image denoising algorithm. The derived function is the distortion lower bound given the mutual information between the original image and the denoised image. The noise environment in this paper is relied on the Poisson noise. The denoised image qualities of different DCNN configurations are compared with the results from the numerical Blahut-Arimoto algorithm. The experimental results indicate that the DCNN models provide near optimal denoised image qualities given mutual information, even though there are some rooms to further improve image denoising algorithm. © 2020 IEEE.

Keywords

Blahut-Arimoto algorithm, deep convolutional neural networks