Regularization method preserving photometry for Richardson-Lucy restoration

Emmanuel Bratsolis; Marc Sigelle

doi:10.1117/12.454142

28 January 2002 Regularization method preserving photometry for Richardson-Lucy restoration

Emmanuel Bratsolis, Marc Sigelle

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454142
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

The success of Richardson-Lucy (RL) algorithm is that it forces the restored image to be non-negative and to conserve global flux at each iteration. The problem with RL algorithm is that it produces solutions that are highly unstable, with high peaks and deep valleys. Our aim is to modify RL algorithm in order do regularize it while preserving positivity and total photometry as far as possible. Data instances that are not compatible with others can cause singularities in the restoration solution. So, we have an ill-posed problem and a regularization method is needed to replace it to a well-posed problem. The regularization approach overcomes this difficulty by choosing among the possible objects one 'smooth' that approximate the data. The basic underlying idea in most regularization approaches is the incorporation of 'a priori' knowledge into the restoration. In this article we try to give a simple method of spatial regularization deriving from RL algorithm in order to overcome the problem of noise amplification during the image reconstruction process. It is very important in astronomy and remote sensing to regularize images while having under control their photometric behavior. We propose a new reconstruction method preserving both the global photometry and local photometric aspects.

Citation Download Citation

Emmanuel Bratsolis and Marc Sigelle "Regularization method preserving photometry for Richardson-Lucy restoration", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454142

ACCESS THE FULL ARTICLE

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