Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN

Ryo Abiko, Masaaki Ikehara

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

2 Citations (Scopus)

Abstract

The removal of mixed noise is a stiff problem since the distribution of the noise cannot be predicted accurately. The most common mixed noise is the combination of Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). Many methods first attempt to remove IN but it might collapse the texture of the image. In this paper, we propose a new learning-based method using convolutional neural network (CNN) for removing mixed gaussian-impulse noise. Since our denoising network can remove various level of mixed noise, neither the preprocessing for removing IN nor noise-level estimation is necessary.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1717-1721
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 2019 May 1
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 2019 May 122019 May 17

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period19/5/1219/5/17

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Keywords

  • convolutional neural network
  • deep learning
  • Image denoising
  • Mixed noise

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Abiko, R., & Ikehara, M. (2019). Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 1717-1721). [8683878] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683878