Abstract
Existing edge detection methods often suffer from noise amplification and excessive retention of non-salient details, limiting their applicability in high-precision industrial scenarios. To address these challenges, we propose CAM-EDIT, a novel framework that integrates Channel Attention Mechanism (CAM) and Edge Detection via Independence Testing (EDIT). The CAM module adaptively enhances discriminative edge features through multi-channel fusion, while the EDIT module employs region-wise statistical independence analysis (using Fisher's exact test and chi-square test) to suppress uncorrelated this http URL experiments on BSDS500 and NYUDv2 datasets demonstrate state-of-the-art performance. Among the nine comparison algorithms, the F-measure scores of CAM-EDIT are 0.635 and 0.460, representing improvements of 19.2\% to 26.5\% over traditional methods (Canny, CannySR), and better than the latest learning based methods (TIP2020, MSCNGP). Noise robustness evaluations further reveal a 2.2\% PSNR improvement under Gaussian noise compared to baseline methods. Qualitative results exhibit cleaner edge maps with reduced artifacts, demonstrating its potential for high-precision industrial applications.
Abstract (translated)
现有的边缘检测方法常常会放大噪声，并过度保留不重要的细节，这限制了它们在高精度工业场景中的应用。为了应对这些挑战，我们提出了CAM-EDIT这一新框架，该框架结合了通道注意机制（Channel Attention Mechanism, CAM）和基于独立性测试的边缘检测（Edge Detection via Independence Testing, EDIT）。其中，CAM模块通过多通道融合自适应增强辨识度高的边缘特征，而EDIT模块则利用区域统计独立性分析（采用费舍尔精确检验和卡方检验）来抑制不相关的噪声。在BSDS500和NYUDv2数据集上的实验表明，该框架达到了最先进的性能水平。 与九种对比算法相比，CAM-EDIT的F-measure分数分别为0.635和0.460，在传统方法（如Canny, CannySR）的基础上分别提高了19.2%至26.5%，优于最新的基于学习的方法（如TIP2020, MSCNGP）。噪声鲁棒性评估进一步显示，在高斯噪声下，CAM-EDIT相比基线方法PSNR值提升了2.2%。定性的结果显示，边缘图更清晰且减少了伪影，表明它在高精度工业应用中具有巨大潜力。
URL
https://arxiv.org/abs/2505.01040
