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Blurring

The blurring characteristics of a shift-invariant linear system are characterized by the point spread function (PSF) of the system. The observed image, , is the result of convolving the PSF, , with the true image, :

Since convolution in space is equivalent to multiplication in frequency, the blurred image can be also be obtained as follows:

where capitals and denote the Fourier spectra of images and , respectively.

A diffraction-limited Hubble Space Telescope (HST) would have a PSF just over 2.5 pixels in radius (0 1 at 633 nm) which would include half of the second bright Airy ring. The actual PSF is over 68 pixels in radius (3 0) due to the spherical aberration caused by malformation of the HST's primary mirror (Burrows 1991).

The PSF of an imaging system can be estimated mathematically or empirically. The computational construction of a PSF involves complicated and computationally demanding ray tracing methods based on a complete description of the optical system. Empirical estimates of the PSF can often be obtained by imaging a relatively bright, isolated point source. For example, an empirical estimate of the PSF of a fluorescence microscope can be obtained by imaging a tiny polystyrene bead coated with a fluorescent marker dye (Fay 1986). PSFs derived by either the computational or empirical approach can be used in iterative deblurring methods. Inaccuracies in the PSF estimate will cause some aspects of the true image data to be interpreted as image noise and limit the degree to which the image can be deblurred.

A PSF can be summarized compactly by encircled energy, the integral of the PSF as a function of distance, , from the center of the PSF. Indexing the PSF using polar coordinates with the origin at the center of the PSF, the encircled energy is

Since optical systems do not add energy to a signal, as . Thus, a spatially extensive PSF attenuates the signal at the center, in-focus location, complicating detection of faint structures in complex images. In crowded fields, the blurred images of nearby objects can interfere with each other, requiring detection algorithms to distinguish intensity peaks due to objects from intensity peaks of artifacts arising from constructive interference between the blurred images of separate objects. Nonlinear algorithms are prone to create false objects at intensity peaks due to constructive interference or to overfitting particularly bright noise peaks.

The effect of a deblurring algorithm can be summarized by plotting the encircled energy of a point source as a function of radius. This measure is computed in the same manner as above with the polar coordinate system centered on the correct location of the point source.



Next: Inverse Filtering Up: Iterative/Recursive Deconvolution with Application Previous: Introduction


rlw@
Thu Jun 2 16:01:49 EDT 1994