A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter

TL;DR

This paper introduces a multiscale gradient fusion approach combined with CBM3D filtering for improved edge detection in color images, enhancing detail preservation and noise robustness compared to existing methods.

Contribution

The paper proposes a novel multiscale gradient fusion method integrated with CBM3D filtering to improve edge detection quality in noisy color images.

Findings

Outperforms Color Sobel, Canny, SE, and AGDD in edge detection quality.

Demonstrates high noise robustness and detail preservation.

Achieves better PR curve, AUC, PSNR, MSE, and FOM metrics.

Abstract

In this paper, a color edge detection strategy based on collaborative filtering combined with multiscale gradient fusion is proposed. The block-matching and 3D (BM3D) filter are used to enhance the sparse representation in the transform domain and achieve the effect of denoising, whereas the multiscale gradient fusion makes up for the defect of loss of details in single-scale edge detection and improves the edge detection resolution and quality. First, the RGB images in the dataset are converted to XYZ color space images through mathematical operations. Second, the colored block-matching and 3D (CBM3D) filter are used on the sparse images and to remove noise interference. Then, the vector gradients of the color image and the anisotropic Gaussian directional derivative of the two scale parameters are calculated and averaged pixel-by-pixel to obtain a new edge strength map. Finally, the edge features are enhanced by image normalization and non-maximum suppression technology, and on that basis, the edge contour is obtained by double threshold selection and a new morphological refinement method. Through an experimental analysis of the edge detection dataset, the method proposed has good noise robustness and high edge quality, which is better than the Color Sobel, Color Canny, SE and Color AGDD as shown by the PR curve, AUC, PSNR, MSE, and FOM indicators.