Abstract
Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schrödinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schrödinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection.
Abstract (translated)
边缘检测是图像处理中的核心环节，然而现有的方法往往面临着关键的局限性。传统的深度学习边缘检测方法需要大量的训练数据集和精细调整，而经典技术在复杂或噪声环境中常常表现不佳，限制了其实际应用效果。为了克服这些局限性，我们提出了一种无需训练、受量子启发的边缘检测模型。我们的方法结合了经典的Sobel边缘检测算法、薛定谔波动方程细化以及将Canny和Laplacian算子相结合的混合框架。 通过消除对训练的需求，该模型变得轻量且易于适应各种应用场景。薛定谔波动方程通过对基于梯度的边缘图进行迭代扩散来精炼其细节，显著提高了边缘检测的精确度。混合框架进一步增强了模型，通过协同结合局部和全局特征，在挑战性条件下确保了其鲁棒性。 在BIPED、Multicue和NYUD等数据集上的广泛评估显示，所提出的模型实现了卓越的表现，并达到了包括ODS（Object Detection Score）、OIS（Object Integrity Score）、AP（Average Precision）以及F-measure在内的各项指标的最新水平。噪声鲁棒性的实验强调了该模型的可靠性，展示了其在实际场景中的实用性。 鉴于其灵活和适应性强的特点，我们的模型非常适合医疗成像、自主系统和环境监测等应用领域，并为边缘检测设定了新的基准标准。
URL
https://arxiv.org/abs/2501.18929
