Generalised blind sampling of images

Blind sampling is a sampling scheme which uses no 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 computing an inner product between the image and some mask. The context of this paper is second order statistical models for images. We discuss the reconstruction of images from arbitrary generalised sampling and present criteria for selecting an optimal mask from a dictionary (family) of masks, or from the set of all possible masks. The latter case leads to PCA. We further propose adaptive sampling schemes that produce different sets of sampling masks for different images, and experimentally verify the advantage of the adaptive schemes over nonadaptive ones.