Adaptively varying transform size selection by ICI rule for transform domain image denoising

The local adaptive processing of signals and images in a transform domain within a sliding window suggests certain advantages in some signal and image de-noising applications due to incorporating an available a priori information about the signals and noises. However, an optimum transform size is also data dependent and generally is not known in advance. Performing the de-noising with the varying transform size suggests further improvements. The approach based on the intersection of con¿dence intervals (ICI) rule for a selection of the varying transform size is introduced.