Adaptive noise smoothing algorithm based on nonstationary correlation assumption

Various noise smoothing algorithms based on the nonstationary image model have been proposed. In most conventional nonstationary image models, however; the pixels are assumed to be uncorrelated to each other in order not to increase the computational burden too much. As a result, some detailed information is lost in the filtered results. We propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that the image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean square error filter. The justification for the proposed image model is presented and the effectiveness of the proposed algorithm is demonstrated experimentally.