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Fast and stable bayesian image expansion using sparse edge priors.

Ashish Raj1, Kailash Thakur

  • 1Center for Imaging of Neurodegenerative Diseases, University of California at San Francisco, VA Medical Center (114M), San Francisco, CA 94121, USA. ashish.raj@ucsf.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 5, 2007
PubMed
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This study introduces a novel edge-driven model for image expansion, significantly reducing blurring and preserving high-frequency details. The efficient algorithm offers a promising alternative to existing edge-preserving methods.

Area of Science:

  • Image processing
  • Computer vision
  • Computational imaging

Background:

  • Traditional image expansion methods often introduce blurring, degrading perceptual quality.
  • Existing edge-preserving techniques have limitations in statistical rigor, ad hoc nature, or computational efficiency.

Purpose of the Study:

  • To develop a new edge-driven stochastic prior model for image expansion.
  • To obtain the maximum a posteriori (MAP) estimate using this model.
  • To present an efficient algorithm for dyadic image expansion.

Main Methods:

  • Proposed a novel edge-driven stochastic prior image model.
  • Derived the maximum a posteriori (MAP) estimate.
  • Developed an efficient algorithm leveraging Fourier transform and prior sparsity for dyadic expansion.

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Main Results:

  • The proposed MAP estimate effectively preserves edges and high-frequency content, avoiding perceptual blurring.
  • The efficient algorithm significantly speeds up the expansion process.
  • Visual and quantitative comparisons show superior performance over existing methods.

Conclusions:

  • The new edge-driven model and efficient algorithm offer a powerful and computationally attractive solution for edge-preserving image expansion.
  • This approach overcomes limitations of previous methods, demonstrating significant potential in image processing applications.