JSM 2005 Online Program

Abstract #304747

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	199
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistical Computing
Abstract - #304747
Title:	Image Reconstruction: An Information-theoretic Approach
Author(s):	Amos Golan*+ and Avinash Bhati and Bahattin Buyuksahin
Companies:	American University and The Urban Institute and World Bank
Address:	, Bethesda, MD, 20816,
Keywords:	entropy ; Information ; Image Recomstruction ; Estimation
Abstract:	Often, we are faced with noisy data or images. At times, we have additional data that can be incorporated into our estimation or image reconstructions, but other times we just have the noisy (or blurry) image. In this work, we develop an information-theoretic (IT) estimation method for reconstructing blurry and noisy images. The resulting method extends (and builds on the foundations of) IT methods by further relaxing some of the underlying assumptions, uses minimal distributional assumptions, performs well (relative to current methods of estimation and image reconstruction), and uses all the available information (hard and soft data) efficiently. In other words, the IT framework discussed and developed here allows us to introduce different priors and other soft data into the estimation process while keeping the complexity of the estimation model to a minimum. In addition, to gain more precision, rather than estimating the signal as "point" estimates, we estimate the full distribution of each pixel. This method is computationally and statistically efficient. The same method also is used for estimating a large class of linear problems.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program