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A New Local Optimal Spline Wavelet for Image Edge Detection

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Publicado en:Mathematics vol. 13, no. 1 (2025), p. 42
Autor principal: Zhou, Dujuan
Otros Autores: Yuan, Zizhao, Cai, Zhanchuan, Zhu, Defu, Shen, Xiaojing
Publicado:
MDPI AG
Materias:
Computational mathematics
Monte Carlo simulation
Accuracy
Wavelet transforms
Image reconstruction
Fourier transforms
Image filters
Noise reduction
Neural networks
Signal processing
Fractals
Algorithms
Computer vision
Methods
Localization
Image processing
Pattern recognition
Morphology
Edge detection
Acceso en línea:Citation/Abstract
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Resumen:Wavelet-based edge detection methods have evolved significantly over the years, contributing to advances in image processing, computer vision, and pattern recognition. This paper proposes a new local optimal spline wavelet (LOSW) and the dual wavelet of the LOSW. Then, a pair of dual filters can be obtained, which can provide distortion-free signal decomposition and reconstruction, while having stronger denoising and feature capture capabilities. The coefficients of the pair of dual filters are calculated for image edge detection. We propose a new LOSW-based edge detection algorithm (LOSW-ED), which introduces a structural uncertainty–aware modulus maxima (SUAMM) to detect highly uncertain edge samples, ensuring robustness in complex and noisy environments. Additionally, LOSW-ED unifies multi-structure morphology and modulus maxima to fully exploit the complementary properties of low-frequency (LF) and high-frequency (HF) components, enabling multi-stage differential edge refinement. The experimental results show that the proposed LOSW and LOSW-ED algorithm has better performance in noise suppression and edge structure preservation.
ISSN:2227-7390
DOI:10.3390/math13010042
Fuente:Engineering Database