Kalman filtering in two dimensions: Further results

Woods, John W.; Ingle, Vinay K.

doi:10.1109/tassp.1981.1163533

Cited by 178 publications

(61 citation statements)

References 9 publications

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“…To compare the results with those of the other methods, the image was filtered using the scalar reduced update Kalman filter (RUKF) [3] and the block diagonal nonsymmetric half-plane filter (BDNSHP) [5] (see Figs. 7 and 8).…”

Section: Implementation and Resultsmentioning

confidence: 99%

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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: Implementation and Resultsmentioning

confidence: 99%

“…This, obviously, leads to an excessively large amount of storage and computations. A number of researchers introduced various filtering schemes [3]- [6] to overcome these problems. The idea of the reduced update Kalman filtering (RUKF) [4]- [6] is to partition the state vector into two segments-the "local state" and the "global state."…”

Section: Introductionmentioning

confidence: 99%

A full-plane block Kalman filter for image restoration

Citrin

Azimi-Sadjadi

1992

IEEE Trans. on Image Process.

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“…The RUKF [5], [6] is a scalar minimum variance estimator which under the constraint that the updating occurs on the closest neighbors provides suboptimal estimates very efficiently.…”

Section: Reduced Update Kalman Filter (Rukf)mentioning

confidence: 99%

“…The selection of the image model can then be made based upon the output of the network. A bank of five reduced update Kalman filter (RUKF) [5], [6] is used to perform the filtering operation. The combined results of these filters when used in conjunction with the BPNN provide restored images with substantially improved quality.…”

Section: Introductionmentioning

confidence: 99%

Neural network decision directed edge-adaptive Kalman filter for image estimation

Azimi-Sadjadi

Xiao²,

Yu³

1999

IEEE Trans. on Image Process.

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Abstract-A neural network-based scheme for decision directed edgeadaptive Kalman filtering is introduced in this work. A backpropagation neural network makes the decisions about the orientation of the edges based on the information in a window centered at the current pixel being processed. Then based upon the neural network output an appropriate image model which closely matches the local statistics of the image is chosen for the Kalman filter. This prevents the oversmoothing of the edges, which would have otherwise been caused by the standard Kalman filter. Simulation results are presented which show the effectiveness of the proposed scheme.

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“…The computational saving in this method was accomplished by limiting the updating process in the Kalman filter to a certain region in the vicinity of the point currently being processed. Later, Woods and Ingle [5] extended the RUKF to the case of degradation due to both blur and random noise. Over the past few years, several other authors have proposed different new 2-D Kalman filtering schemes for restoration of images degraded by both blur and noise [6]- [8].…”

mentioning

confidence: 99%