Ms. Zhiying Wen Profile

Zhiying Wen

at Univ of New South Wales/ADFA

SPIE Involvement:

Author

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 | 15 September 2008 Paper

Restoration of atmospherically degraded images using a sparse prior

Zhiying Wen, Donald Fraser, Andrew Lambert

Proceedings Volume 7073, 70731P (2008) https://doi.org/10.1117/12.793570

KEYWORDS: Deconvolution, Image registration, Prototyping, Point spread functions, Atmospheric turbulence, Astronomical imaging, Turbulence, Imaging systems, Telescopes, Image processing

Read Abstract +

The reconstruction of turbulence-affected images has been an active research topic in the field of astronomical imaging. Many approaches have been proposed in the literature. Recently, researchers have extended the methods to the recovery of long-path territorial natural scene surveillance, which is affected even more by air turbulence. Some approaches from astronomical imaging also work well in the latter problem. However, although these methods have involved statistics, such as a statistical model of atmospheric turbulence or the probability distribution of photons forming an image, they have not taken account of the statistical properties of natural scenes observed in long-path horizontal imagery. Recent research by others has made use of the fact that a real world image generally has a sparse distribution of its derivatives. In this paper, we investigate algorithms with such a constraint imposed during the restoration of turbulence-affected images. This paper proposes an iterative, blind deconvolution algorithm that follows a registration and averaging method to remove anisoplanatic warping in a time sequence of degraded images. The use of a sparse prior helps to reduce noise, produce sharper edges and remove unwanted artifacts in the estimated image for the reason that it pushes only a small number of pixels to have non-zero (or large) derivatives. We test the new algorithm with simulated and natural data and experiments show that it performs well.

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