TY - GEN
T1 - Coupled convolution method for image processing
AU - Fan, Wen
AU - Liang, Junli
AU - Li, Pengliang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - This paper develops a Coupled Convolution Model (CCM) for Error Concealment (EC), Image Super Resolution (ISR) and Multifocus Image Fusion (MIF) used in unmanned aerial vehicles. The key points of this paper are: I) for EC, to efficiently exploit the inherent correlation relationship between the lost region and its known adjacent region, this paper introduces a special CCM in order that their convolution model results via different convolution filters are close to the counterpart on the convolution space; ii) for ISR, we develop CCM to train two convolution filters and map the original low-resolution(LR) and high-resolution(HR) image patches onto a unified convolution subspace. iii) as for MIF, since image fusion depends on the local information of source images resulting from the same scene, this paper constructs a special Coupled Two-Dimensional Convolution Model (2D-CCM) for the image patches from different source images. Experimental results show that the proposed CCM is very effective for EC, ISR and MIF.
AB - This paper develops a Coupled Convolution Model (CCM) for Error Concealment (EC), Image Super Resolution (ISR) and Multifocus Image Fusion (MIF) used in unmanned aerial vehicles. The key points of this paper are: I) for EC, to efficiently exploit the inherent correlation relationship between the lost region and its known adjacent region, this paper introduces a special CCM in order that their convolution model results via different convolution filters are close to the counterpart on the convolution space; ii) for ISR, we develop CCM to train two convolution filters and map the original low-resolution(LR) and high-resolution(HR) image patches onto a unified convolution subspace. iii) as for MIF, since image fusion depends on the local information of source images resulting from the same scene, this paper constructs a special Coupled Two-Dimensional Convolution Model (2D-CCM) for the image patches from different source images. Experimental results show that the proposed CCM is very effective for EC, ISR and MIF.
KW - coupled convolution
KW - error concealment
KW - image fusion
KW - super resolution
KW - unmanned aerial vehicles
UR - https://www.scopus.com/pages/publications/85050864945
U2 - 10.1109/ICUS.2017.8278399
DO - 10.1109/ICUS.2017.8278399
M3 - 会议稿件
AN - SCOPUS:85050864945
T3 - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
SP - 512
EP - 516
BT - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
A2 - Xu, Xin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Y2 - 27 October 2017 through 29 October 2017
ER -