Mr. Marcio de Moraes Marim Profile

Marcio de Moraes Marim

PhD Student at Institut Pasteur

SPIE Involvement:

Author

Websites:

Company Website

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 September 2009 Paper

Compressed sensing in biological microscopy

Marcio de Moraes Marim, Elsa Angelini, Jean-Christophe Olivo-Marin

Proceedings Volume 7446, 744605 (2009) https://doi.org/10.1117/12.826275

KEYWORDS: Signal to noise ratio, Denoising, Microscopy, Luminescence, Interference (communication), Image restoration, Image acquisition, Compressed sensing, Molecules, Process control

Read Abstract +

Noise level and photobleaching are cross-dependent problems in biological fluorescence microscopy. Indeed, observation of fluorescent molecules is challenged by photobleaching, a phenomenon whereby the fluorophores are degraded by the excitation light. One way to control this process is by reducing the intensity of the light or the time exposure, but it comes at the price of decreasing the signal-to-noise ratio (SNR). Although a host of denoising methods have been developed to increase the SNR, most are post-processing techniques and require full data acquisition. In this paper we propose a novel technique, based on Compressed Sensing (CS) that simultaneously enables reduction of exposure time or excitation light level and improvement of image SNR. Our CS-based method can simultaneously acquire and denoise data, based on statistical properties of the CS optimality, noise reconstruction characteristics and signal modeling applied to microscopy images with low SNR. The proposed approach is an experimental optimization combining sequential CS reconstructions in a multiscale framework to perform image denoising. Simulated and practical experiments on fluorescence image data demonstrate that thanks to CS denoising we obtain images with similar or increased SNR while still being able to reduce exposure times. Such results open the gate to new mathematical imaging protocols, offering the opportunity to reduce photobleaching and help biological applications based on fluorescence microscopy.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years