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When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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Related Experiment Video

Updated: Oct 22, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
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The Empirical Watershed Wavelet.

Basile Hurat1, Zariluz Alvarado1, Jérôme Gilles1

  • 1Department of Mathematics & Statistics, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182-7720, USA.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the empirical watershed wavelet transform, an adaptive multi-resolution analysis tool. It enables 2D empirical wavelet filters using arbitrary frequency domain partitions for improved image analysis.

Keywords:
deconvolutionempirical waveletscale-spacetexture segmentationwatershed

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Area of Science:

  • Signal Processing
  • Image Analysis
  • Computer Vision

Background:

  • Empirical wavelet transform (EWT) is an adaptive multi-resolution analysis tool.
  • Existing 2D EWT extensions face limitations due to constrained partitioning shapes in the Fourier domain.

Purpose of the Study:

  • To develop a novel 2D empirical wavelet transform capable of utilizing arbitrary partitions of the frequency domain.
  • To introduce an algorithm for detecting these partitions from image spectra.

Main Methods:

  • Theoretical development for constructing 2D empirical wavelet filters based on arbitrary frequency domain partitions.
  • Algorithm combining scale-space representation and watershed transform for partition detection from image spectra.
  • Definition of the empirical watershed wavelet transform.

Main Results:

  • Demonstration of the effectiveness of the empirical watershed wavelet transform on toy images.
  • Successful application of the transform in unsupervised texture segmentation.
  • Effective use of the transform in image deconvolution tasks.

Conclusions:

  • The proposed empirical watershed wavelet transform overcomes limitations of existing 2D EWT methods.
  • The developed algorithm facilitates adaptive partitioning for enhanced image analysis.
  • The transform shows significant advantages in texture segmentation and image deconvolution.