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AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Bi-exponential edge-preserving smoother.

Philippe Thévenaz1, Daniel Sage, Michael Unser

  • 1École polytechnique fédérale de Lausanne, Lausanne, Switzerland. philippe.thevenaz@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 31, 2012
PubMed
Summary
This summary is machine-generated.

We introduce a new edge-preserving smoother, the bi-exponential edge-preserving smoother (BEEPS), which offers significant computational and memory savings. This lean algorithm achieves results comparable to traditional bilateral filters at a fraction of the cost.

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Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Edge-preserving smoothing is crucial in image processing.
  • Traditional methods like the bilateral filter are computationally expensive.
  • There is a need for efficient edge-preserving smoothing algorithms.

Purpose of the Study:

  • To present a computationally lean edge-preserving smoother.
  • To demonstrate its effectiveness and low resource requirements.
  • To establish its relationship with existing filters.

Main Methods:

  • Development of a lean algorithm inspired by the bi-exponential filter.
  • Implementation using a pair of one-tap recursions.
  • Local adaptation of filter weights to image data.

Main Results:

  • The proposed bi-exponential edge-preserving smoother (BEEPS) has a low memory and computational footprint.
  • BEEPS achieves results similar to the bilateral filter.
  • The algorithm requires minimal coding effort and has a constant per-pixel cost.

Conclusions:

  • Efficient edge-preserving smoothing is achievable without high computational cost.
  • BEEPS offers a practical and effective alternative to traditional methods.
  • The algorithm's simplicity and efficiency make it highly valuable for various applications.