Committee ChairFrieden, B. Roy
Schowengerdt, Robert A.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractIntegral logarithmic transforms are defined for both one-dimensional and two-dimensional input functions. These have the desirable properties of linearity and invariance to scale change of the input. Two-dimensional integral logarithmic transform is additionally invariant to rotation. The integral logarithmic transforms are conveniently inverted by simple differentiation. Second, a new approach is given for the problem of reconstruction of phase from modulus data. A set of Wiener-filter functions is formed that multiply, in turn, displaced versions of the modulus data in frequency space such that the sum is a minimum L₂-error norm solution for the object. The required statistics are power spectra of the signal and noise, and correlation between modulus data at a given frequencies and complex object spectral values at adjacent frequencies. Finally, a new technique is proposed to reconstruct a turbulent image from a superposition model. Imagery through random atmospheric turbulence is modeled as a stochastic superposition process. By this model, each short-exposure point spread function is a superposition of randomly weighted and displaced versions of one intensity profile. If we could somehow estimate the weights and displacements for a given image, then by the superposition model we would known the spread function, and consequently, could invert the imaging equation for the object.
Degree ProgramElectrical and Computer Engineering