Title :
Nonstationary filtering for removal of signal dependent noise in reconstructions from projections
Author :
Sauver, K. ; Liu, Bede
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
Abstract :
The authors discuss a way to improve transmission tomograms in the presence of large density data variations and low photon dosage. They present a design for nonstationary filters that exploits the detectability of high-density regions in the object to match filters to local noise properties. The design of each filter is formulated as a least-squares optimization problem, with linear equality and inequality constraints that protect the resolution of detail while minimizing noise power in the reconstruction. Experimental results show reconstructions in whitened noise of power equivalent to those of conventional convolution backprojection, but with significantly better resolution
Keywords :
computerised tomography; filtering and prediction theory; least squares approximations; picture processing; convolution backprojection; high-density regions; large density data variations; least-squares optimization; linear equality constraints; linear inequality constraints; local noise properties; low photon dosage; noise power; nonstationary filtering; nonstationary filters; signal dependent noise; transmission tomograms; whitened noise; Computed tomography; Convolution; Detectors; Filtering; Image reconstruction; Matched filters; Object detection; Single photon emission computed tomography; Statistics; X-ray imaging;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on
Conference_Location :
Glasgow
DOI :
10.1109/ICASSP.1989.266719
