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Inverse halftoning via MAP estimation.

R L Stevenson1

  • 1Dept. of Electr. Eng., Notre Dame Univ., IN.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study introduces new algorithms for reconstructing continuous-tone images from halftoned versions. The methods use maximum a posteriori estimation and Markov random fields for accurate image reconstruction, including edges.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Image halftoning aims for accurate gray-level representation using limited palettes.
  • Reconstructing continuous-tone images from halftoned versions is an inverse problem.

Purpose of the Study:

  • To develop efficient algorithms for high-quality continuous-tone image reconstruction from halftoned originals.
  • To address the inverse problem of image halftoning.

Main Methods:

  • Utilizing maximum a posteriori (MAP) estimation criteria.
  • Employing a Markov random field (MRF) model for prior image distribution.
  • Developing algorithms for image reconstruction from dithered and error-diffused halftones.

Main Results:

Related Experiment Videos

  • Proposed algorithms accurately reconstruct smooth image regions.
  • Discontinuities along image edges are well-preserved.
  • Demonstrated effective gray-level reconstructions from various halftone types.

Conclusions:

  • The MAP estimation with MRF model provides accurate image reconstruction.
  • The technique is applicable to rescreening and processing of halftone images.
  • Efficient continuous-tone image recovery from halftones is achieved.