Facial Image Denoising Using Convolutional Autoencoder Network

Noise effects can interfere the face recognition process in outdoor conditions. Therefore, image denoising topic is the classical issue in the field of image processing and computer vision subjects. In this paper, we show that the solution of denoising process using the autoencoder networks based on...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) S. 1 - 5
Hauptverfasser: Tun, Naing Min, Gavrilov, Alexander I., Tun, Nyan Linn
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2020
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Noise effects can interfere the face recognition process in outdoor conditions. Therefore, image denoising topic is the classical issue in the field of image processing and computer vision subjects. In this paper, we show that the solution of denoising process using the autoencoder networks based on the ORL face database. The proposed method can support face recognition systems designed for use in an outdoor environment as the preprocessing stage and it can provide the effective results after training process.
AbstractList Noise effects can interfere the face recognition process in outdoor conditions. Therefore, image denoising topic is the classical issue in the field of image processing and computer vision subjects. In this paper, we show that the solution of denoising process using the autoencoder networks based on the ORL face database. The proposed method can support face recognition systems designed for use in an outdoor environment as the preprocessing stage and it can provide the effective results after training process.
Author Tun, Naing Min
Tun, Nyan Linn
Gavrilov, Alexander I.
Author_xml – sequence: 1
  givenname: Naing Min
  surname: Tun
  fullname: Tun, Naing Min
  organization: Bauman Moscow State Technical University,Moscow,Russia
– sequence: 2
  givenname: Alexander I.
  surname: Gavrilov
  fullname: Gavrilov, Alexander I.
  organization: Bauman Moscow State Technical University,Moscow,Russia
– sequence: 3
  givenname: Nyan Linn
  surname: Tun
  fullname: Tun, Nyan Linn
  organization: Bauman Moscow State Technical University,Moscow,Russia
BookMark eNotj0FOwzAQRY0EC1o4AZtcIGFm7NT2BikKLUQqsKHrynEmlUVqozQFcXsQ7ea_zdOT_kxcxhRZiAyhQAR739TNsnpRRi1MQUBQWEQCAxdihpoMqtICXYuHlfPBDVmzdzvOHjmmcAhxl23-t07xKw3HKaT451THKXH0qeMxe-XpO40fN-Kqd8OBb8-ci81q-V4_5-u3p6au1nkgkFOO2PtOSd1JbxU4ubAlOUuOyq4jKZ2W6Ixlrdi3tm9bZzxpQ60H0GSxlXNxd-oGZt5-jmHvxp_t-ZL8BQ-HRqQ
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICIEAM48468.2020.9112080
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 1728145902
9781728145907
EndPage 5
ExternalDocumentID 9112080
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i203t-11fcd437d3c940a36952a92a25dd233a731a89e74ecb9fbba8c2782bc007291b3
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000607234900210&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Thu Jun 29 18:37:54 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-11fcd437d3c940a36952a92a25dd233a731a89e74ecb9fbba8c2782bc007291b3
PageCount 5
ParticipantIDs ieee_primary_9112080
PublicationCentury 2000
PublicationDate 2020-May
PublicationDateYYYYMMDD 2020-05-01
PublicationDate_xml – month: 05
  year: 2020
  text: 2020-May
PublicationDecade 2020
PublicationTitle 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
PublicationTitleAbbrev ICIEAM
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.780414
Snippet Noise effects can interfere the face recognition process in outdoor conditions. Therefore, image denoising topic is the classical issue in the field of image...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Autoencoder
convolutional neural networks
Face recognition
image denoising
Image recognition
Industrial engineering
Neural networks
Noise reduction
Training
Title Facial Image Denoising Using Convolutional Autoencoder Network
URI https://ieeexplore.ieee.org/document/9112080
WOSCitedRecordID wos000607234900210&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB7a4sGTSiu-2YNH024e2yQXodQWC1p6UOmt5LXQg7tSt_39Jtm1InjxFsIkIc8vyXwzA3DrMUVzpS2yghjEuNBIEJwjwkRqnQdkEcMBvT3x-Vwsl3LRgru9LYxzLpLPXD8koy7flmYbvsoGfmMSf8NpQ5vzYW2r9U3OSeVgNp5NRs_MA2qgbJG034j_ipsSYWN69L8Gj6H3Y3-XLPbIcgItV3ThfqrC93Yye_dHQPLginId3vlJVPonvuSuWUVeZrStyuCh0rpNMq-J3j14nU5exo-oiX6A1iSlFcI4N5ZRbqmRLFV0KDOiJFEks5ZQqjjFSkjHmTNa5lorYYiHe22iM3Cs6Sl0irJwZ36ElNMyzc1QScwyjrXWFme-epoHu1d1Dt3Q99VH7eBi1XT74u_sSzgMw1uz_q6gU2227hoOzK5af25u4qx8AfE_j1A
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JTwIxFG4QTfSkBoy7c_BoocsMbS8mBCFMhAkHNNxIt0k4MGNw4PfbzowYEy_emqZLXrev7fveewA8OkxRTCoDDScahowryAlOIQk5MtYBMi_DAb1PWJLwxULMGuBpbwtjrS3JZ7bjk6Uu3-R667_Kum5jEnfDOQCHPnIWqqy1vuk5SHTjQTzsT0MHqZ60RVCnrvArckoJHKPT_3V5Bto_FnjBbI8t56BhsxZ4Hkn_wR3Ea3cIBC82y1f-pR-Uav_A1dzV68iV6W-L3PuoNHYTJBXVuw3eRsP5YAzr-AdwRRAtIMapNiFlhmoRIkl7IiJSEEkiYwilklEsubAstFqJVCnJNXGAr3TpDhwregGaWZ7ZSzdC0iqBUt2TAocRw0opgyPXPE295au8Ai0v-_KjcnGxrMW-_jv7ARyP59PJchInrzfgxA91xQG8Bc1is7V34EjvitXn5r6coS-U35KW
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2020+International+Conference+on+Industrial+Engineering%2C+Applications+and+Manufacturing+%28ICIEAM%29&rft.atitle=Facial+Image+Denoising+Using+Convolutional+Autoencoder+Network&rft.au=Tun%2C+Naing+Min&rft.au=Gavrilov%2C+Alexander+I.&rft.au=Tun%2C+Nyan+Linn&rft.date=2020-05-01&rft.pub=IEEE&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FICIEAM48468.2020.9112080&rft.externalDocID=9112080