Abstract
Edge detection is crucial in image processing, but existing methods often produce overly detailed edge maps, affecting clarity. Fixed-window statistical testing faces issues like scale mismatch and computational redundancy. To address these, we propose a novel Multi-scale Adaptive Independence Testing-based Edge Detection and Denoising (EDD-MAIT), a Multi-scale Adaptive Statistical Testing-based edge detection and denoising method that integrates a channel attention mechanism with independence testing. A gradient-driven adaptive window strategy adjusts window sizes dynamically, improving detail preservation and noise suppression. EDD-MAIT achieves better robustness, accuracy, and efficiency, outperforming traditional and learning-based methods on BSDS500 and BIPED datasets, with improvements in F-score, MSE, PSNR, and reduced runtime. It also shows robustness against Gaussian noise, generating accurate and clean edge maps in noisy environments.
Abstract (translated)
边缘检测在图像处理中至关重要，但现有方法往往会产生过于详细的边缘图，影响清晰度。固定窗口统计测试面临尺度不匹配和计算冗余等问题。为解决这些问题，我们提出了一种基于多尺度自适应独立性检验的边缘检测与去噪（EDD-MAIT）的新方法。这是一种结合了通道注意力机制和独立性测试的多尺度自适应统计测试基边检测方法。该方法采用梯度驱动的自适应窗口策略动态调整窗口大小，从而提高细节保留能力和噪声抑制能力。 EDD-MAIT在BSDS500和BIPED数据集上表现出更好的鲁棒性、准确性和效率，在F-score、MSE（均方误差）、PSNR（峰值信噪比）等指标上有显著改善，并且运行时间更短。此外，该方法对高斯噪声具有较强的鲁棒性，能够在嘈杂环境中生成精确而干净的边缘图。
URL
https://arxiv.org/abs/2505.01032
