Implementation of Image Enhancement and Least Squares Filtering Techniques for Acoustic Data
Two complementary problems in image processing are addressed in this paper, these are concerned with the restoration of blurred and noise corrupted images; and the enhancement of resolution when images are collected over limited apertures, as in many acoustic applications.
Kalman filtering techniques are discussed and shown to facilitate desmearing of space variant and invariant blurs, as well as to suppress noise. 1-D smearing effects are tackled within a Gauss-Bernoulli frame work for data observed over a circulant aperture.
Several two dimensional techniques are included for the enhancement of spectral resolution. These are based on extensions of 2-D MEM spectral estimation schemes. Further, a set ofARMA type models are considered for aperture extension when associated sensor arrays have limited coverage.
KeywordsEntropy Covariance Autocorrelation Convolution Boulder
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