Two-dimensional block Kalman filtering for image restoration
Date
1987
Authors
Azimi-Sadjadi, Mahmood R., author
Wong, Ping Wah, author
IEEE, publisher
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Abstract
This paper is concerned with developing an efficient two-dimensional (2-D) block Kalman filtering for digital image restoration. A new 2-D multiinput, multioutput (MIMO) state-space structure for modeling the image generation process is introduced. This structure is derived by arranging a vector autoregressive (AR) model with a causal quarter-plane region of support in block form. This model takes into account the correlations of the image data in successive neighboring blocks and, as a result, reduces the edge effects prominent in the available Kalman strip filtering techniques. The degradation model for an infinite extent Linear space invariant (LSI) blur and white Gaussian (WG) noise is also modeled by an MIMO block state-space equation stemmed from a single-input single-output (SISO) 2-D state-space structure. The image generation model and the degradation model are combined to yield a composite block-state dynamic structure. The block Kalman filtering equations are obtained for this dynamic structure and then used to compute the suboptimal filter estimates of a noisy and blurred image.
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Subject
white noise
picture processing
Kalman filters
two-dimensional digital filters