Abstract
New filter classes for multichannel image processing are introduced and analyzed. The proposed methodology constitutes a unifying and powerful framework for multichannel image processing. The new filters use fuzzy membership functions based on different distance measures among the image vectors to adapt to local data in the image. The principle behind the new nonlinear filters is explained in detail. Using the proposed methodology multichannel nonlinear problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated results. The new approach provides insight into the nature of the filtering process and the structure of the underlying nonlinear operator. The special case of color image processing is studied as an important example of multichannel image processing. Simulation results indicate that the new filters are computationally attractive and have excellent performance.
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