Histogram-based fuzzy colour filter for image restoration

  • Authors:

  • Stefan Schulte;Valérie De Witte;Mike Nachtegael;Dietrich Van der Weken;Etienne E. Kerre

  • Affiliations:

  • Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (Building S9), B-9000 Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (Building S9), B-9000 Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (Building S9), B-9000 Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (Building S9), B-9000 Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (Building S9), B-9000 Gent, Belgium

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2007

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Abstract

A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches).