FIRE operators for image processing

Fuzzy inference ruled by else-action (FIRE) operators are a class of nonlinear operators which process image data by using fuzzy reasoning. The latest developments in the field of FIRE operators are presented in this work focusing on two very important research and application areas: nonlinear filtering of noisy images and edge detection. First, a new family of filters for images corrupted by impulse noise is presented. Due to the adoption of piecewise linear fuzzy sets, the proposed approach is able to combine noise cancellation and detail preservation. A method for automatic generation of the fuzzy rulebase using the Genetic Algorithms is also presented. Then, a new class of noise-protected operators for edge detection is proposed. By suitably choosing fuzzy sets and fuzzy aggregation mechanism, these operators are able to detect edges in images corrupted by different noise distributions. Many experimental results are reported showing that the proposed operators perform significantly better than other techniques in the literature.

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