A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal

Abstract

Fuzzy sets provide a framework for incorporating human knowledge as an efficient unsupervised machine learning tool for problem solving. The approach discussed in this paper introduces a generalized transfer learning scheme using rule based fuzzy logic for edge detection in digital images. The spatial domain statistical properties of the image are explored as training data set and expressed in fuzzy format to obtain a decision function for optimal edge detection along with reduction of impulse noise. During fuzzy inference process, a specific linguistic value in input fuzzy set is selected in order to obtain an optimal range of second order difference which discriminates the edge pixels from the non-edge pixels. The proposed fuzzy rule based optimal edge pixel detection method in the presence of random valued impulse noise tends to sufficiently extract the edge pixels with out boosting the noisy pixels. The effectiveness of the proposed fuzzy rule based edge detection scheme is verified by testing it on various standard test images and comparing with existing edge detection techniques at different noise densities.