A full plane block Kalman filter for image estimation

Citrin, S.; Azimi-Sadjadi, M.R.

doi:10.1109/iscas.1990.112192

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…In order to complete the modeling process the off-diagonal submatrices QUij, i "* j, have to be determined. For the QUS6 matrix, post-multiply both sides of (9) by Xr(k) and use the orthogonality principle,…”

Section: Pij(!)~e[xi(n)\j(n -I)]mentioning

confidence: 99%

A full-plane block Kalman filter for image restoration

Citrin

Azimi-Sadjadi

1992

IEEE Trans. on Image Process.

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Abstract-A new two-dimensional (2-D) block Kalman filtering method is presented which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur. Causality is maintained within the filtering process by employing multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods.

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Section: Pij(!)~e[xi(n)\j(n -I)]mentioning

confidence: 99%

A full-plane block Kalman filter for image restoration

Citrin

Azimi-Sadjadi

1992

IEEE Trans. on Image Process.

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Filtering of images corrupted by multiplicative and white plus coloured additive noises using covariance information

Nakamori¹,

García-Ligero²,

Hermoso-Carazo³

et al. 2008

Mathematical and Computer Modelling

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Two-dimensional adaptive block Kalman filtering of SAR imagery

Azimi-Sadjadi

Bannour

1991

IEEE Trans. Geosci. Remote Sensing

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Abstract-Speckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is mtroduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise.

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A full plane block Kalman filter for image estimation

Cited by 5 publications

References 12 publications

A full-plane block Kalman filter for image restoration

A full-plane block Kalman filter for image restoration

Filtering of images corrupted by multiplicative and white plus coloured additive noises using covariance information

Two-dimensional adaptive block Kalman filtering of SAR imagery

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