Image estimation using fast modified reduced update Kalman filter

The authors have proposed some modifications of the reduced update Kalman filter (RUKF) as applied to filtering of images corrupted by additive noise. They have reduced the computational complexity by reducing the state dimensionality. By doing so, it is shown that the computational requirement is reduced by an order of magnitude while the loss of performance is only marginal. Next, the RUKF is modified using the score function based approach to accommodate non-Gaussian noise. The image is modeled as a nonstationary mean and stationary variance autoregressive Gaussian process. It is shown that the stationary variance assumption is reasonable if the nonstationary mean is computed by an edge and detail preserving efficient estimator of local nonstationary mean. Such an estimator, called the hybrid multistage medium D (HMSMD) filter, is also described. Detailed experimental results are provided which indicate the success of the new filtering scheme