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A Multiscale Gradient Fusion Method for Color Image Edge Detection Using CBM3D Filtering.

Zhunruo Feng1, Ruomeng Shi2, Yuhan Jiang3

  • 1School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new color edge detection technique using collaborative filtering and multiscale gradient fusion. The method enhances image quality and noise resistance, outperforming existing approaches.

Keywords:
CBM3D filterXYZ color modelanisotropic Gaussian derivativeimage edge detection

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Traditional edge detection methods struggle with noise and detail loss in color images.
  • Existing techniques often fail to preserve fine edge details across different scales.
  • Color information is crucial for accurate object boundary identification.

Purpose of the Study:

  • To develop a novel color edge detection method integrating collaborative filtering and multiscale gradient fusion.
  • To improve edge resolution, noise resistance, and overall image quality in color edge detection.
  • To provide a robust alternative to conventional color edge detection algorithms.

Main Methods:

  • Utilized Block-Matching and 3D (BM3D) filter for sparse representation enhancement and noise reduction.
  • Applied Colored Block-Matching and 3D (CBM3D) filter in the XYZ color space.
  • Computed vector gradients and anisotropic Gaussian directional derivatives for refined edge strength mapping.
  • Employed normalization, non-maximum suppression, double-thresholding, and morphological refinement for edge enhancement and extraction.

Main Results:

  • The proposed method demonstrated robust noise resistance and superior edge quality.
  • Experimental results showed improved performance over traditional methods like Color Sobel, Color Canny, SE, and Color AGDD.
  • Performance was validated using metrics including the PR curve, AUC, PSNR, MSE, and FOM.

Conclusions:

  • The novel color edge detection method effectively integrates collaborative filtering and multiscale gradient fusion.
  • The technique significantly enhances edge resolution and overall image quality while maintaining noise resistance.
  • The proposed approach represents a substantial advancement in color edge detection technology.