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Restoration of randomly blurred images with measurement error in the point spread function

University of British Columbia

The restoration of images degraded by a stochastic, time varying point spread func-tion(PSF) is addressed. The object to be restored is assumed to remain fixed during the observation time. A sequence of observations of the unknown object is assumed available. The true value of the random PSF is not known. However, for each observation a "noisy" measurement of the random PSF at the time of observation is assumed available. Practical applications in which the PSF is time varying include situations in which the images are obtained through a nonhomogeneous medium such as water or the earth's atmosphere. Under such conditions, it is not possible to determine the PSF in advance, so attempts must be made to extract it from the degraded images themselves. A measurement of the PSF may be obtained by either isolating a naturally occurring point object in the scene, such as a reference star in optical astronomy, or by artificially installing an impulse light source in the scene. The noise in the measurements of point spread functions obtained in such a manner are particularly troublesome in cases when the light signals emitted by the point object are not very strong. In this thesis, we formulate a model for this restoration problem with PSF measurement error. A maximum likelihood filter and a Wiener filter are then developed for this model. Restorations are performed on simulated degraded images. Comparisons are made with standard filters of the classical restoration model(ignoring the PSF error), and also with results based on the averaged degraded image and averaged PSF's. Experimental results confirm that the filters we developed perform better than those based on averaging and than those ignoring the PSF measurement error.

Text

2010-10-28

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http://hdl.handle.net/2429/29629

Electrical and Computer Engineering

University of British Columbia

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