Mr. Thomas A. Baran Profile

Thomas A. Baran

at Tufts Univ

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 July 2004 Paper

Denoising of images using logical (binary) transforms

Sos Agaian, Thomas Baran, Karen Panetta

Proceedings Volume 5438, (2004) https://doi.org/10.1117/12.538763

KEYWORDS: Transform theory, Digital filtering, Binary data, Detection and tracking algorithms, Image filtering, Image compression, Denoising, Sensors, Signal to noise ratio, Interference (communication)

Read Abstract +

Because a signal can often be easily corrupted during its transmission, registration, or storage, de-noising is an important field in the areas of communications systems and of signal and image processing, especially where defense and security applications are of concern. Techniques employing transform-based methods such as the Fourier transform, the cosine transform, and wavelets have already been applied successfully to this field when dealing with an image corrupted by noise having a Gaussian or uniform distribution. However, images where impulse or salt and pepper noise are introduced are typically treated using median or switched-median algorithms because the sudden discontinuities of impulse noise often present problems for conventional transform-based noise reduction approaches. Additionally, binary images cannot easily be de-noised by fast orthogonal transforms or wavelets. A novel noise detection and reduction scheme using a fast logical (binary) transform-based Boolean minimization algorithm is presented. The presented approach is capable of de-noising both binary and multivalued images corrupted by impulse noise. A comparison with well-known methods is offered. Particularly, the algorithm reliably detects noise more effectively than existing switched-median methods, and de-noising results comparable to or better than those attainable with median filtering are possible. The technique performs especially well when operating on images of high complexity. The new technique does not require the use of a multiplication nor a sorting operation. In addition, we show that the presented de-noising procedure could be easily performed on an already compressed file or during the compression step. Furthermore, the simplicity of the transform makes a gate-level hardware realization practical for use with distributed sensors and inexpensive or high-speed imaging systems.

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