Statistics – Applications
Scientific paper
Nov 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004spie.5607...26r&link_type=abstract
Wavelet Applications in Industrial Processing II. Edited by Truchetet, Frederic; Laligant, Olivier. Proceedings of the SPIE, Vol
Statistics
Applications
Scientific paper
Image degradation is a frequently encountered problem in different imaging systems, like microscopy, astronomy, digital photography, etc. The degradation is usually modeled as a convolution with a blurring kernel (or Point Spread Function, psf) followed by noise addition. Based on the combined knowledge about the image degradation and the statistical features of the original images, one is able to compensate at least partially for the degradation using so-called image restoration algorithms and thus retrieve information hidden for the observer. One problem is that often this blurring kernel is unknown, and has to be estimated before actual image restoration can be performed. In this work, we assume that the psf can be modeled by a function with a single parameter, and we estimate the value of this parameter. As an example of such a single-parametric psf, we have used a Gaussian. However, the method is generic and can be applied to account for more realistic degradations, like optical defocus, etc.
Philips Wilfried R.
Portilla Javier
Rooms Filip
No associations
LandOfFree
Parametric PSF estimation via sparseness maximization in the wavelet domain does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Parametric PSF estimation via sparseness maximization in the wavelet domain, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Parametric PSF estimation via sparseness maximization in the wavelet domain will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1057605