Title :
Intensity estimation from shot-noise data
Author :
Sequeira, Raúl E. ; Gubner, John A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
fDate :
6/1/1995 12:00:00 AM
Abstract :
The estimation of the intensity function of a Poisson-driven shot-noise process is addressed using a regularization technique, where the data is modeled as a signal term plus a signal-dependent noise term. A new data-based method for selecting a pair of regularization parameters is presented and compared with the minimum unbiased risk method. The detail in the intensity function can be recovered by both methods, but the new method does a better job at suppressing spurious oscillations
Keywords :
parameter estimation; shot noise; signal sampling; stochastic processes; Poisson-driven shot-noise process; data-based method; intensity function estimation; minimum unbiased risk method; regularization parameters; regularization technique; sampled data; shot-noise data; signal term; signal-dependent noise term; spurious oscillations suppression; Expectation-maximization algorithms; Image reconstruction; Image restoration; Integral equations; Military computing; Nonlinear filters; Random variables; Signal processing; Smoothing methods; Statistics;
Journal_Title :
Signal Processing, IEEE Transactions on
