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A New Class Biorthogonal Spline Wavelet for Image Edge Detection

2024-06-12 14:50:40
Dujuan Zhou, Zizhao Yuan

Abstract

Spline wavelets have shown favorable characteristics for localizing in both time and frequency. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCSSW), based on the Cohen-Daubechies-Feauveau wavelet construction method and the cubic special spline algorithm. BCSSW has better properties in compact support, symmetry, and frequency domain characteristics. However, current mainstream detection operators usually ignore the uncertain representation of regional pixels and global structures. To solve these problems, we propose a structural uncertainty-aware and multi-structure operator fusion detection algorithm (EDBSW) based on a new BCSSW spline wavelet. By constructing a spline wavelet that efficiently handles edge effects, we utilize structural uncertainty-aware modulus maxima to detect highly uncertain edge samples. The proposed wavelet detection operator utilizes the multi-structure morphological operator and fusion reconstruction strategy to effectively address anti-noise processing and edge information of different frequencies. Numerous experiments have demonstrated its excellent performance in reducing noise and capturing edge structure details.

Abstract (translated)

分割波浪格式在本地化在时间和频率方面显示出有利的特性。在本文中,我们提出了一种新的biorthogonal立方特分割波浪(BCSSW),基于Cohen-Daubechies-Feauveau波浪构建方法和立方特分割算法。BCSSW在紧凑支持、对称和频域特征方面具有更好的性能。然而,当前的主流检测器通常忽略了区域像素的不确定表示和全局结构。为解决这些问题,我们提出了一个基于新BCSSW波浪的具有结构不确定性感知和多结构操作符融合检测算法(EDBSW)。通过构建一个有效处理边缘效应的曲线波浪,我们利用结构不确定性感知余弦值最大值来检测高度不确定边缘样本。所提出的波浪检测器利用多结构形态操作符和融合重构策略来有效地解决抗噪声处理和不同频率的边缘信息。大量实验证明其在降噪和捕捉边缘结构细节方面具有优异性能。

URL

https://arxiv.org/abs/2406.08285

PDF

https://arxiv.org/pdf/2406.08285.pdf


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