|M.Sc Student||Devir Zvi|
|Subject||Generalized Blind Sampling of Images|
|Department||Department of Computer Science||Supervisor||Professor Michael Lindenbaum|
|Full Thesis text|
Blind sampling is a sampling scheme which works without any knowledge about the image except for the measurements it obtains. An adaptive blind sampling scheme makes use of that knowledge to wisely choose the next sample. In this work we consider generalised sampling, where each measurement is obtained by an inner product between the image and some mask.
In the first part of this work, we discuss the reconstruction of images from arbitrary generalised samples, under second-order statistical models. We consider algebraic and statistical schemes for reconstruction and the connection between them. We then show an optimal nonadaptive method for choosing generalised sampling masks in this context.
In the second part, we limit ourselves to a particular set of wavelet masks and propose a blind adaptive progressive sampling scheme. The scheme estimates the magnitudes of the unsampled wavelet coefficients and samples the coefficients with larger estimated magnitude first. With this method, the selected masks extract the image information much more efficiently than nonadaptive schemes and get closer to the optimal, non-blind results.