Detection of Gaussian noise and its level using deep Convolutional Neural Network

Joon Huang Chuah, Hui Ying Khaw, Foo Chong Soon, Chee Onn Chow

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

11 Citations (Scopus)

Abstract

This study presents a Convolutional Neural Network (CNN) model to effectively recognize the presence of Gaussian noise and its level in images. The existing denoising approaches are mostly based on an assumption that the images to be processed are corrupted with noises. This work, on the other hand, aims to intelligently evaluate if an image is corrupted, and to which level it is degraded, before applying denoising algorithms. We used 12000 and 3000 standard test images for training and testing purposes, respectively. Different noise levels are introduced to these images. The overall accuracy of 74.7% in classifying 10 classes of noise levels are obtained. Our experiments and results have proven that this model is capable of performing Gaussian noise detection and its noise level classification.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE Region 10 Conference (TENCON)
EditorsMohammad Faizal Ahmad Fauzi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2447-2450
Number of pages4
ISBN (Electronic)9781509011339, 9781509011346
ISBN (Print)9781509011353
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Tencon (IEEE Region 10 Conference) 2017 - Penang, Malaysia
Duration: 5 Nov 20178 Nov 2017
https://ieeexplore.ieee.org/xpl/conhome/8169968/proceeding (Proceedings)
https://ieeemy.org/tencon/ (Website)

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2017-December
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

ConferenceIEEE Tencon (IEEE Region 10 Conference) 2017
Abbreviated titleTENCON 2017
CountryMalaysia
CityPenang
Period5/11/178/11/17
Internet address

Keywords

  • convolutional neural networks
  • Gaussian noise
  • image noise
  • noise detection
  • training

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