Low level image processing and analysis using radius filters

Abstract

A novel class of nonlinear L-filters based on local statistics is introduced, which will be called Radius Filters (RFs). The underlying idea for the construction of RFs is the sorting of the values of the input signal belonging in a sliding window, according to their distance from their mean value, in a vector Tr that carries significant signal information, while the filter output is defined as a linear combination of the elements of the vector Tr. RFs are simple, intuitive and easy to implement while they offer a unique signal formation, which provides insight and in depth signal interpretation for the proper filter design. Moreover, they may considered as a generalization of the OS Filters, since OS filters may be easily produced from the RFs. A number of low level image processing applications of the RFs such as impulse noise removal, speckle noise removal, adapting filtering, edge detection in noisy images, image sharpening and image frequency decomposition are outlined.