A fast adaptive approach to the restoration of images degraded by noise

Abstract An adaptive approach to the restoration of noisy images is presented in this paper. Two recursive schemes are developed, which simultaneously estimate the unknown image model and restore the image. In the first, the reduced update Kalman filter (RUKF) is appropriately combined with a fast multichannel space-recursive estimation technique (FAMSRET). The computational load associated with the filter is significantly reduced in the second scheme. A low-order state-space image model is derived for this purpose. A Kalman filter, combined with the FAMSRET algorithm, is then applied to this model providing a fast adaptive technique for the suppression of noise in images. Examples are given, using real image data, which illustrate the performance of the proposed schemes.

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