|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ИНХС РАН |
||
Constructing Optimal Convolution Kernel for Image Processing with Imperfect Information on Original Distortionтезисы доклада
- Авторы: Peter Golubtsov, Kevin Joyce, Johnathan Bardsley
- Сборник: Pacific Northwest Section of the MATHEMATICAL ASSOCIATION OF AMERICA
- Тезисы
- Год издания: 2014
- Место издания: University of Montana Missoula, Montana USA
- Первая страница: 26
- Последняя страница: 26
- Аннотация: We study a problem of designing a two-dimensional Point Spread Function (PSF) with a given radius R. Such a function will be optimal for estimating an unknown signal f from an observation g = a * f +n. Here the PSF a represents a convolution-type distortion and n is random noise. It is shown that if the additive noise is uncorrelated, then the optimization problem reduces to a one-dimensional Fredholm equation of the second kind on [0;R]. There are many ways to solve such problems numerically. However, it might be more natural to construct the PSF r in a certain finite-dimensional class of functions from the very beginning. Such a problem reduces to a set of linear equations. This technique is then generalized to the case when the PSF a is not known precisely.
- Добавил в систему: Голубцов Петр Викторович