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Texture synthesis with grouplets.

Gabriel Peyré1

  • 1CNRS and Ceremade, Université Paris-Dauphine, CEREMADE, Place du Maréchal De Lattre De Tassigny, 75775 Paris Cedex 16, France. gabriel.peyre@ceremade.dauphine.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel geometric texture synthesis and inpainting method using a new grouplet transform. This approach models textures via orientation flow and elongated stroke atoms for realistic pattern generation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Existing texture synthesis methods often struggle with capturing complex geometric patterns.
  • Modeling natural textures requires adaptable representations that can handle variations in scale and orientation.

Purpose of the Study:

  • To propose a novel method for geometric texture synthesis and inpainting.
  • To introduce a new grouplet transform adapted for natural texture modeling.
  • To enable the generation of diverse texture patterns through statistical modeling and optimization.

Main Methods:

  • A texture model incorporating a geometric layer driving a new grouplet transform.
  • Utilizing an orientation flow to capture texture patterns.
  • Developing grouplet atoms as elongated strokes aligned with the geometric flow.
  • Employing statistical modeling and sparsity optimization on grouplet coefficients.

Main Results:

  • The grouplet transform extends Mallat's construction, effectively modeling natural textures.
  • Grouplet atoms with varying lengths and widths accurately match diverse image structures.
  • Successful texture synthesis and inpainting achieved through joint optimization of flow and coefficients.

Conclusions:

  • The proposed method offers a robust framework for synthesizing and inpainting geometric textures.
  • The novel grouplet transform and orientation flow provide an effective representation for natural textures.
  • This approach advances the capabilities of texture analysis and generation in computer vision.