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Shape preserving local histogram modification.

V Caselles1, J L Lisani, J M Morel

  • 1Dept. of Math. and Inf., Univ. de les Illes Balears, Palma de Mallorca, Spain. dmivca0@ps.uib.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This paper introduces a new method for contrast enhancement that preserves image shapes. Unlike other methods, it avoids adding false details by equalizing histograms within connected image components.

Area of Science:

  • Image Processing
  • Computer Vision
  • Digital Image Analysis

Background:

  • Traditional local histogram equalization methods often fail to preserve image structures.
  • This can lead to the introduction of spurious objects and alteration of essential image information.
  • Maintaining the integrity of image features during contrast enhancement is crucial for accurate analysis.

Purpose of the Study:

  • To present a novel shape-preserving contrast enhancement technique.
  • To address the limitations of existing local contrast enhancement algorithms.
  • To ensure the preservation of image information and structural integrity.

Main Methods:

  • A local histogram equalization algorithm is developed.
  • The algorithm operates on connected components defined by grey-values and spatial relationships.

Related Experiment Videos

  • Mathematical morphology principles are integrated to define these components.

    • Main Results:

    • The proposed method effectively enhances image contrast.
    • Crucially, it preserves the level-sets and shapes of image features.
    • Demonstrated success on both grey-value and color images.

    Conclusions:

    • The novel approach successfully achieves shape-preserving contrast enhancement.
    • It overcomes the shortcomings of conventional local schemes by maintaining image information.
    • The method offers a robust solution for applications requiring accurate image structure representation.