用于图像边缘检测的一类新型双正交样条小波
A New Class Biorthogonal Spline Wavelet for Image Edge Detection
AI总结：
本文构造新型双正交三次特殊样条小波 BCSSW，并提出结构不确定性感知与多结构算子融合的 EDBSW 边缘检测算法，提升抗噪性与多频率边缘细节捕捉能力。
摘要：
样条小波在时域和频域定位方面均表现出良好特性。本文基于 Cohen-Daubechies-Feauveau 小波构造方法和三次特殊样条算法，提出一种新型双正交三次特殊样条小波（BCSSW）。BCSSW 在紧支撑、对称性和频域特性方面具有更优性质。然而，当前主流检测算子通常忽略区域像素和全局结构的不确定性表示。为解决这些问题，本文基于新型 BCSSW 样条小波，提出一种结构不确定性感知与多结构算子融合检测算法（EDBSW）。通过构造可高效处理边缘效应的样条小波，我们利用结构不确定性感知模极大值来检测高度不确定的边缘样本。所提出的小波检测算子利用多结构形态学算子和融合重建策略，有效处理抗噪过程以及不同频率的边缘信息。大量实验证明，该算法在降低噪声和捕捉边缘结构细节方面具有优异性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
