Parametric PSF estimation via sparseness maximization in the wavelet domain

Filip Rooms; Wilfried R. Philips; Javier Portilla

doi:10.1117/12.571539

1 November 2004 Parametric PSF estimation via sparseness maximization in the wavelet domain

Filip Rooms, Wilfried R. Philips, Javier Portilla

Proceedings Volume 5607, Wavelet Applications in Industrial Processing II; (2004) https://doi.org/10.1117/12.571539
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

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.

Citation Download Citation

Filip Rooms, Wilfried R. Philips, and Javier Portilla "Parametric PSF estimation via sparseness maximization in the wavelet domain", Proc. SPIE 5607, Wavelet Applications in Industrial Processing II, (1 November 2004); https://doi.org/10.1117/12.571539

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