Adaptive schemes for noise filtering and edge detection by use of local statistics

Some adaptive schemes for noise filterings and edge detection of digital signals are developed. They are bases on the minimum-mean-square-error estimate of the information-bearing signal corrupted by additive noise. The estimate is computed using the local statistics of the input signal and noise. The output is fed back to the input, and the difference between the input and the output is used as the noise estimator. The local statistics of signal and noise are computed through a moving signal window and a moving noise window, which are over the input signal and the noise estimator, respectively. These schemes change their performance according to the local signal-to-noise ratio adaptively. Two kinds of adaptive filtering algorithms and an edge detection algorithm are considered. Their performance in the presence of noise is evaluated and compared to the performance of some other methods. Simulation results on one-dimensional signals and real images are presented