Neural network deinterlacing using multiple fields

Hyunsoo Choi, Eunjae Lee, Chulhee Lee

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

6 Citations (Scopus)

Abstract

In this paper, we proposed a deinterlacing algorithm using neural networks for conversion of interlaced videos to progressive videos. The proposed method uses multiple fields: a previous field, a current field, and a next field. Since the proposed algorithm uses multiple fields, the neural network is able to take into account the motion pattern which might exists in adjacent fields. Experimental results demonstrate that the proposed algorithm provides better performances than existing neural network deinterlacing algorithms that uses a single field.

Original languageEnglish
Title of host publicationIntelligent Computing in Signal Processing and Pattern Recognition
Subtitle of host publicationInternational Conference on Intelligent Computing, ICIC 2006
EditorsDe-Shaung Huang, Kang Li, George William Irwin
Pages970-975
Number of pages6
DOIs
Publication statusPublished - 2006 Sep 26

Publication series

NameLecture Notes in Control and Information Sciences
Volume345
ISSN (Print)0170-8643

Fingerprint

neural network
video
performance

All Science Journal Classification (ASJC) codes

  • Library and Information Sciences

Cite this

Choi, H., Lee, E., & Lee, C. (2006). Neural network deinterlacing using multiple fields. In D-S. Huang, K. Li, & G. W. Irwin (Eds.), Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006 (pp. 970-975). (Lecture Notes in Control and Information Sciences; Vol. 345). https://doi.org/10.1007/11816515_122
Choi, Hyunsoo ; Lee, Eunjae ; Lee, Chulhee. / Neural network deinterlacing using multiple fields. Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006. editor / De-Shaung Huang ; Kang Li ; George William Irwin. 2006. pp. 970-975 (Lecture Notes in Control and Information Sciences).
@inproceedings{fbe640c7ceee499f9e00c9392cbbacf3,
title = "Neural network deinterlacing using multiple fields",
abstract = "In this paper, we proposed a deinterlacing algorithm using neural networks for conversion of interlaced videos to progressive videos. The proposed method uses multiple fields: a previous field, a current field, and a next field. Since the proposed algorithm uses multiple fields, the neural network is able to take into account the motion pattern which might exists in adjacent fields. Experimental results demonstrate that the proposed algorithm provides better performances than existing neural network deinterlacing algorithms that uses a single field.",
author = "Hyunsoo Choi and Eunjae Lee and Chulhee Lee",
year = "2006",
month = "9",
day = "26",
doi = "10.1007/11816515_122",
language = "English",
isbn = "3540372571",
series = "Lecture Notes in Control and Information Sciences",
pages = "970--975",
editor = "De-Shaung Huang and Kang Li and Irwin, {George William}",
booktitle = "Intelligent Computing in Signal Processing and Pattern Recognition",

}

Choi, H, Lee, E & Lee, C 2006, Neural network deinterlacing using multiple fields. in D-S Huang, K Li & GW Irwin (eds), Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006. Lecture Notes in Control and Information Sciences, vol. 345, pp. 970-975. https://doi.org/10.1007/11816515_122

Neural network deinterlacing using multiple fields. / Choi, Hyunsoo; Lee, Eunjae; Lee, Chulhee.

Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006. ed. / De-Shaung Huang; Kang Li; George William Irwin. 2006. p. 970-975 (Lecture Notes in Control and Information Sciences; Vol. 345).

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

TY - GEN

T1 - Neural network deinterlacing using multiple fields

AU - Choi, Hyunsoo

AU - Lee, Eunjae

AU - Lee, Chulhee

PY - 2006/9/26

Y1 - 2006/9/26

N2 - In this paper, we proposed a deinterlacing algorithm using neural networks for conversion of interlaced videos to progressive videos. The proposed method uses multiple fields: a previous field, a current field, and a next field. Since the proposed algorithm uses multiple fields, the neural network is able to take into account the motion pattern which might exists in adjacent fields. Experimental results demonstrate that the proposed algorithm provides better performances than existing neural network deinterlacing algorithms that uses a single field.

AB - In this paper, we proposed a deinterlacing algorithm using neural networks for conversion of interlaced videos to progressive videos. The proposed method uses multiple fields: a previous field, a current field, and a next field. Since the proposed algorithm uses multiple fields, the neural network is able to take into account the motion pattern which might exists in adjacent fields. Experimental results demonstrate that the proposed algorithm provides better performances than existing neural network deinterlacing algorithms that uses a single field.

UR - http://www.scopus.com/inward/record.url?scp=33748855981&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33748855981&partnerID=8YFLogxK

U2 - 10.1007/11816515_122

DO - 10.1007/11816515_122

M3 - Conference contribution

AN - SCOPUS:33748855981

SN - 3540372571

SN - 9783540372578

T3 - Lecture Notes in Control and Information Sciences

SP - 970

EP - 975

BT - Intelligent Computing in Signal Processing and Pattern Recognition

A2 - Huang, De-Shaung

A2 - Li, Kang

A2 - Irwin, George William

ER -

Choi H, Lee E, Lee C. Neural network deinterlacing using multiple fields. In Huang D-S, Li K, Irwin GW, editors, Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006. 2006. p. 970-975. (Lecture Notes in Control and Information Sciences). https://doi.org/10.1007/11816515_122