TY - GEN
T1 - Designing a composite dictionary adaptively from joint examples
AU - Wang, Zhangyang
AU - Yang, Yingzhen
AU - Yang, Jianchao
AU - Huang, Thomas
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/4/21
Y1 - 2016/4/21
N2 - We study the complementary behaviors of external and internal examples in image restoration, and are motivated to formulate a composite dictionary design framework. The composite dictionary consists of the global part learned from external examples, and the sample-specific part learned from internal examples. The dictionary atoms in both parts are further adaptively weighted to emphasize their model statistics. Experiments demonstrate that the joint utilization of external and internal examples leads to substantial improvements, with successful applications in image denoising and super resolution.
AB - We study the complementary behaviors of external and internal examples in image restoration, and are motivated to formulate a composite dictionary design framework. The composite dictionary consists of the global part learned from external examples, and the sample-specific part learned from internal examples. The dictionary atoms in both parts are further adaptively weighted to emphasize their model statistics. Experiments demonstrate that the joint utilization of external and internal examples leads to substantial improvements, with successful applications in image denoising and super resolution.
UR - http://www.scopus.com/inward/record.url?scp=84978959053&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978959053&partnerID=8YFLogxK
U2 - 10.1109/VCIP.2015.7457839
DO - 10.1109/VCIP.2015.7457839
M3 - Conference contribution
AN - SCOPUS:84978959053
T3 - 2015 Visual Communications and Image Processing, VCIP 2015
BT - 2015 Visual Communications and Image Processing, VCIP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Visual Communications and Image Processing, VCIP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -