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Uniform Depth Channel Flow01:27

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic

Yudai Yamaguchi1, Ichiro Yoshida2, Yuki Kondo3

  • 1Graduate School of Science and Engineering, Research Institute of Precision Engineering and Data Science, Hosei University, Tokyo, Japan.

Scientific Reports
|April 4, 2023
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Summary
This summary is machine-generated.

A new method automatically determines the optimal basic width for the fast M-estimation Gaussian filter (FMGFI), improving image denoising and edge preservation. This advance enhances image quality and recognition rates in digital imaging applications.

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Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Digital filtering is crucial for image quality, recognition, and super-resolution, but noise degrades performance.
  • Traditional low-pass filters like Gaussian filters reduce noise but blur edges, hindering feature detection.
  • Previous Fast M-estimation Gaussian Filter for Images (FMGFI) achieved denoising and edge preservation but required manual parameter tuning.

Purpose of the Study:

  • To develop an automated method for determining the optimal basic width of the FMGFI.
  • To overcome the limitations of manual parameter setting for efficient image denoising.
  • To enhance both denoising and edge preservation capabilities in digital image preprocessing.

Main Methods:

  • Proposed a novel approach to automatically calculate the optimal basic width for the FMGFI.
  • Implemented a per-pixel adaptive basic width calculation, unlike previous uniform image-wide settings.
  • Evaluated performance against established filters including non-local mean, Gaussian, median, bilateral, and adaptive bilateral filters.

Main Results:

  • The proposed method demonstrated superior denoising and edge preservation compared to previous FMGFI implementations.
  • Achieved state-of-the-art denoising performance against salt-and-pepper noise among tested filters.
  • Showed comparable denoising and edge preservation to other filters for Gaussian noise in visual evaluations.

Conclusions:

  • The automated basic width determination method significantly improves FMGFI's efficiency and effectiveness.
  • This technique offers enhanced image quality and recognition rates, crucial for various digital imaging tasks.
  • The proposed method is expected to be a valuable contribution to efficient denoising and image quality improvement.