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Exact histogram specification.

Dinu Coltuc1, Philippe Bolon, Jean-Marc Chassery

  • 1Department of Electrical Engineering, Valahia University of Targoviste, 0200 Targoviste, Dambovita, Romania. coltuc@Valahia.ro

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 5, 2006
PubMed
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Exact histogram specification for discrete images is an ill-posed problem. A novel pixel ordering in K-dimensional space enables invertible cumulative distribution functions for improved image processing.

Area of Science:

  • Computer Vision
  • Image Processing
  • Digital Imaging

Background:

  • Statistical models for histogram equalization/specification provide exact results for continuous data.
  • Discrete image histogram specification is an ill-posed problem due to non-invertible cumulative distribution functions.

Purpose of the Study:

  • To address the ill-posed nature of discrete image histogram specification.
  • To develop a method for achieving exact histogram specification in discrete images.
  • To explore applications of the proposed technique in image processing.

Main Methods:

  • Translating the histogram specification problem into a K-dimensional space.
  • Inducing a strict ordering among image pixels that refines the natural ordering.
  • Developing statistical models for the induced ordering.

Related Experiment Videos

Main Results:

  • The proposed K-dimensional space translation and pixel ordering yield invertible cumulative distribution functions.
  • Experimental results validate the effectiveness of the induced ordering for discrete histogram specification.
  • The method demonstrates potential in various image processing applications.

Conclusions:

  • Exact histogram specification for discrete images is achievable through a novel K-dimensional ordering approach.
  • The proposed method overcomes the limitations of traditional discrete histogram equalization.
  • Applications include image enhancement, normalization, and watermarking.