Title :
Two-dimensional frequency-domain blind system identification using higher order statistics with application to texture synthesis
Author :
Chi, Chong-Yung ; Chen, Chii-Horng
Author_Institution :
Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan
fDate :
4/1/2001 12:00:00 AM
Abstract :
In this paper, Shalvi and Weinstein´s (1993) super-exponential (SE) algorithm using higher order statistics for blind deconvolution of one-dimensional (1-D) linear time-invariant systems is extended to a two-dimensional (2-D) SE algorithm. Then, a 2-D frequency-domain blind system identification (BSI) algorithm for 2-D linear shift-invariant (LSI) systems using the computationally efficient 2-D SE algorithm and the 2-D linear prediction error filter is proposed. In addition to the LSI system estimate, the proposed BSI algorithm also provides a minimum mean square error (MMSE) equalizer estimate and an MMSE signal enhancement filter estimate. Then, a texture synthesis method (TSM) using the proposed BSI algorithm is presented. Some simulation results to support the efficacy of the proposed BSI algorithm and some experimental results to support the efficacy of the proposed TSM are presented. Finally, some conclusions are drawn
Keywords :
blind equalisers; filtering theory; frequency-domain analysis; higher order statistics; identification; image texture; least mean squares methods; linear systems; prediction theory; 1D linear time-invariant systems; 2D frequency-domain blind system identification; 2D linear prediction error filter; 2D linear shift-invariant systems; 2D super-exponential algorithm; MMSE equalizer estimate; MMSE signal enhancement filter estimate; computationally efficient 2D SE algorithm; higher order statistics; minimum mean square error; simulation results; texture synthesis; Autoregressive processes; Equations; Filters; Higher order statistics; Large scale integration; Maximum likelihood estimation; Parameter estimation; Signal processing algorithms; System identification; Two dimensional displays;
Journal_Title :
Signal Processing, IEEE Transactions on