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

• URL: http://arxiv.org/abs/2408.14013v2
• Date: Tue, 3 Sep 2024 15:34:09 GMT
• Title: A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter
• Authors: Zhuoyue Wang, Yiyi Tao, Danqing Ma, Jiajing Chen,
• Abstract summary: A color edge detection strategy based on collaborative filtering combined with multiscale gradient fusion is proposed. The block-matching and 3D (CBM3D) filter are used to enhance the sparse representation in the transform domain. The method proposed has good noise robustness and high edge quality, which is better than the Color Sobel, Color Canny, SE and Color AGDD.
• Score: 1.54369283425087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [63.39134873744748]
  Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. This paper proposes a novel noise-resistant M3DM-NR framework to leverage strong multi-modal discriminative capabilities of CLIP. Extensive experiments show that M3DM-NR outperforms state-of-the-art methods in 3D-RGB multi-modal noisy anomaly detection.
  arXiv  Detail & Related papers  (2024-06-04T12:33:02Z)
• Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration [49.004898985671815]
  3DGS is not alias-free, and its rendering at varying resolutions could produce severe blurring or jaggies. This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels. We introduce this approximation in the two-dimensional pixel shading, and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area.
  arXiv  Detail & Related papers  (2024-03-17T02:06:03Z)
• Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution [52.582632746409665]
  We propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. Our method achieves superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-01T06:35:59Z)
• $PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction [97.06927852165464]
  Reconstructing the 3D shape of an object from a single RGB image is a long-standing and highly challenging problem in computer vision. We propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising diffusion process.
  arXiv  Detail & Related papers  (2023-02-21T13:37:07Z)
• Position-Aware Relation Learning for RGB-Thermal Salient Object Detection [3.115635707192086]
  We propose a position-aware relation learning network (PRLNet) for RGB-T SOD based on swin transformer. PRLNet explores the distance and direction relationships between pixels to strengthen intra-class compactness and inter-class separation. In addition, we constitute a pure transformer encoder-decoder network to enhance multispectral feature representation for RGB-T SOD.
  arXiv  Detail & Related papers  (2022-09-21T07:34:30Z)
• Color Image Edge Detection using Multi-scale and Multi-directional Gabor filter [6.56250901439562]
  The main advantage of the proposed method is that high edge detection accuracy is attained while maintaining good noise robustness. The results show that the proposed detector has the better experience in detection accuracy and noise-robustness.
  arXiv  Detail & Related papers  (2022-08-16T02:21:16Z)
• Perceptual Robust Hashing for Color Images with Canonical Correlation Analysis [21.22196411212803]
  We propose a novel perceptual image hashing scheme for color images based on ring-ribbon quadtree and color vector angle. Our scheme has satisfactory performances with respect to robustness, discrimination and security, which can be effectively used in copy detection and content authentication.
  arXiv  Detail & Related papers  (2020-12-08T09:35:21Z)
• Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification [0.0]
  A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image. The FFSIFT-MLPC technique performs three major processes, namely preprocessing, feature extraction and classification using multiple layers. The results evident that presented FFSIFT-MLPC technique improves the hyperspectral image classification accuracy, PSNR and minimizes false positive rate as well as classification time.
  arXiv  Detail & Related papers  (2020-06-18T10:51:04Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)
• Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering [0.0]
  In order to improve speckle noise denoising of block matching 3d filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) has been proposed. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method.
  arXiv  Detail & Related papers  (2020-01-04T08:17:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.