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Graphs based methods for simultaneous smoothing and sharpening.

Cristina Pérez-Benito1, Cristina Jordán2, J Alberto Conejero3

  • 1Instituto de Biomecánica de València, Universitat Politècnica de València, E-46022 València, Spain.

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|March 21, 2020
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Summary
This summary is machine-generated.

We developed two new graph-based methods for simultaneously smoothing and sharpening color images. These techniques analyze local pixel graphs to improve image quality, offering competitive results against current methods.

Keywords:
Color image processingLocal graphsSimultaneous smoothing and sharpening

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Image smoothing and sharpening are crucial for visual quality.
  • Simultaneously achieving both smoothing and sharpening presents a significant challenge in image processing.

Purpose of the Study:

  • To introduce two novel methods, GMS³ and NGMS³, for simultaneous image smoothing and sharpening.
  • To evaluate the performance of these methods against state-of-the-art techniques.

Main Methods:

  • Developing the Graph Method for Simultaneous Smoothing and Sharpening (GMS³) and the Normalized Graph Method for Simultaneous Smoothing and Sharpening (NGMS³).
  • Analyzing local graph structures computed at each pixel based on neighboring pixels in RGB space.
  • Employing a kernel-based filter for smoothing within connected components and modifying pixels to enhance differences between components.

Main Results:

  • The proposed GMS³ and NGMS³ methods demonstrate competitive performance in simultaneous smoothing and sharpening.
  • Parameter tuning involved observer opinions and the BRISQUE image quality assessment score.

Conclusions:

  • The GMS³ and NGMS³ methods offer an effective approach to simultaneously smooth and sharpen color images.
  • The graph-based analysis provides a robust framework for managing these dual image processing objectives.