Novel Fuzzy Filters for Noise Suppression from Digital Grey and Color Images

Images are rich information carriers and (such as medical images) are normally contaminated by additive and substitutive noise which makes the extraction of features (and clinical data analysis) difficult. Hence to enhance the image quality prior to post processing, image pre-processing operations such as de-noising with linear and non-linear filters have been applied traditionally. Recently nonlinear filtering techniques have been assumed a lot of significance as they are capable of suppressing the effects of substitutive (salt and pepper impulsive noise of low to high noise levels) and additive (Gaussian noise of low to medium noise levels) noise types and to preserve the important signal/image details such as edges and fine details and suppress the degradations occurring at the time of image/signal formation or transmission through nonlinear channels, during storage and retrieval. Broadly speaking, image filters exist in transform and spatial domains. Spatial domain nonlinear filters are more versatile than their counterparts, namely linear filters. Spatial domain nonlinear fuzzy classical filters are simply modification/extension of the classical median and moving average filtering approaches, offer several advantages over classical nonlinear filters, and using simple fuzzy rules it is easy