Spatially Structured Sparse Morphological Component Separation for voltage-sensitive dye optical imaging
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- 作者：Hugo Ragueta ; hugo.raguet@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Cyril Monierb ; cyril.monier@unic.cnrs-gif.fr" class="auth_mail" title="E-mail the corresponding author ; Luc Foubertb ; luc.foubert@unic.cnrs-gif.fr" class="auth_mail" title="E-mail the corresponding author ; Isabelle Ferezoub ; isabelle.ferezou@unic.cnrs-gif.fr" class="auth_mail" title="E-mail the corresponding author ; Yves Fregnacb ; yves.fregnac@unic.cnrs-gif.fr" class="auth_mail" title="E-mail the corresponding author ; Gabriel Peyré ; a ; gabriel.peyre@ceremade.dauphine.fr" class="auth_mail" title="E-mail the corresponding author ;
- 关键词：Voltage-sensitive dye optical imaging ; Sensory cortical dynamics ; Orientation maps ; Sparse component separation
- 刊名：Journal of Neuroscience Methods
- 出版年：2016
- 出版时间：15 January 2016
- 年：2016
- 卷：257
- 期：Complete
- 页码：76-96
- 全文大小：6296 K
文摘
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We present a new tool to denoise and remove artifacts from voltage-sensitive dye optical imaging.
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It is based on a mathematical modeling of the signal and artifacts formation process.
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It makes use of state of the art convex optimization techniques.
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An automatic parameter selection process through risk estimation is embedded into the method.
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It is validated on synthetic as well as real VSDOI data.