Filtering heavy noised images using ICI rule for adaptive varying bandwidth selection

A novel approach is developed to solve a problem of varying bandwidth selection for filtering heavily noised signals. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and adaptive to unknown smoothness of the signal. Asymptotic accuracy results as well as simulation demonstrate the efficiency of the approach. In particular, the tenfold SNR improvement is demonstrated for filtering binary images.