TY - GEN
T1 - Neural network deinterlacing using multiple fields
AU - Choi, Hyunsoo
AU - Lee, Eunjae
AU - Lee, Chulhee
PY - 2006
Y1 - 2006
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 -