Linking synaptic computation for image enhancement

Zhan, K; Shi, J; Teng, J; Li, Q; Wang, M; Lu, F

Linking synaptic computation for image enhancement

Zhan, K

Shi, J Teng, J Li, Q Wang, M Lu, F

Permalink

Publication Type:: Journal Article
Citation:: Neurocomputing, 2017, 238 pp. 1 - 12
Issue Date:: 2017-05-17

Closed Access

	Filename	Description	Size
	1-s2.0-S0925231217300930-main.pdf	Published Version	4.65 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhan, K https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Shi, J	en_US
dc.contributor.author	Teng, J	en_US
dc.contributor.author	Li, Q	en_US
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Lu, F	en_US
dc.date.issued	2017-05-17	en_US
dc.identifier.citation	Neurocomputing, 2017, 238 pp. 1 - 12	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125845
dc.description.abstract	© 2017 Elsevier B.V. Linking synaptic computation network is proposed. The linking synapse is introduced into the neural network inspired by the gamma band oscillations in visual cortical neurons, and the neural network is applied to image representation. The linking synaptic mechanism of the network allows integrating temporal and spatial information. An image is input to the network and the enhanced result is obtained by the final linking synaptic state. The visual performance of the results boosts the details while preserving the information in the input image. The effectiveness of the method has been borne out by five quantitative metrics as well as qualitative comparisons with other methods.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2017.01.031	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Linking synaptic computation for image enhancement	en_US
dc.type	Journal Article
utslib.citation.volume	238	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	238	en_US

Abstract:

© 2017 Elsevier B.V. Linking synaptic computation network is proposed. The linking synapse is introduced into the neural network inspired by the gamma band oscillations in visual cortical neurons, and the neural network is applied to image representation. The linking synaptic mechanism of the network allows integrating temporal and spatial information. An image is input to the network and the enhanced result is obtained by the final linking synaptic state. The visual performance of the results boosts the details while preserving the information in the input image. The effectiveness of the method has been borne out by five quantitative metrics as well as qualitative comparisons with other methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125845