Image reconstruction and multidimensional field estimation from randomly scattered sensors.
Many important problems in statistical signal processing can be formulated as function estimation from randomly scattered sensors in a multidimensional space, e.g., image reconstruction from photon-limited images and field estimation from scattered sensors. We present a novel approach to the study of signal reconstruction from random samples in a multidimensional space. In particular, we study a classical iterative reconstruction method and demonstrate that it forms a sequence of unbiased estimates for band-limited signals, which converge to the true function in the mean-square sense. We subsequently rely on the iterative estimation method for multidimensional image reconstruction and field estimation from sensors scattered according to a multidimensional Poisson and uniform distribution. Computer simulation experiments are used to demonstrate the efficiency of the iterative estimation method in image reconstruction and field estimation from randomly scattered sensors.
Pan
Pan
P
Schonfeld
Dan
D
D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers