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The Ellipselet Transform.

Zahra Khodabandeh1,2, Hossein Rabbani1,3, Alireza Mehri Dehnavi1,3

  • 1Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Mashhad, Iran.

Journal of Medical Signals and Sensors
|September 24, 2019
PubMed
Summary
This summary is machine-generated.

The new ellipselet transform offers improved image denoising for circular shapes compared to other methods. While effective for certain images, its performance varies with texture complexity.

Keywords:
Basis functionsX-letscircletellipseletimage denoising

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Area of Science:

  • Image processing
  • Computer vision
  • Mathematical transforms

Background:

  • Many natural images contain circular and elliptical objects, such as biological cell nuclei and parasites.
  • Atomic representations using 2D basis functions like circles and ellipses are valuable for image analysis.
  • The circlet transform was an early development for processing circular shapes.

Purpose of the Study:

  • To extend the circlet transform by incorporating elliptical basis functions.
  • To introduce and evaluate a novel image processing transform based on ellipses.

Main Methods:

  • Development of the ellipselet transform, utilizing elliptical basis functions.
  • Comparative analysis of the ellipselet transform against various X-let transforms.
  • Application of transforms for image denoising, including 2D-discrete wavelet transform, dual-tree complex wavelet, curvelet, contourlet, steerable pyramid, and circlet transform.

Main Results:

  • The ellipselet transform demonstrated superior performance in Peak Signal to Noise Ratio (PSNR) for image denoising under noise levels of 30, particularly for images with circular structures like 'Lena'.
  • Comparative evaluations showed the ellipselet's effectiveness against other geometrical X-let transforms in specific denoising scenarios.
  • For images with complex textures, such as 'Barbara', the ellipselet transform yielded lower PSNR values compared to the dual-tree complex wavelet and steerable pyramid transforms.

Conclusions:

  • The ellipselet transform shows promise as an effective tool for image denoising, especially for images containing significant circular features.
  • Performance of the ellipselet transform is dependent on image characteristics, with varying results for different textures.
  • Further research may be needed to optimize ellipselet transform for diverse image textures and noise conditions.