Sobel

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.

arXiv:2501.18929

Defective Edge Detection Using Cascaded Ensemble Canny Operator

Edge detection has been one of the most difficult challenges in computer vision because of the difficulty in identifying the borders and edges from the real-world images including objects of varying kinds and sizes. Methods based on ensemble learning, which use a combination of backbones and attention modules, outperformed more conventional approaches, such as Sobel and Canny edge detection. Nevertheless, these algorithms are still challenged when faced with complicated scene photos. In addition, the identified edges utilizing the current methods are not refined and often include incorrect edges. In this work, we used a Cascaded Ensemble Canny operator to solve these problems and detect the object edges. The most difficult Fresh and Rotten and Berkeley datasets are used to test the suggested approach in Python. In terms of performance metrics and output picture quality, the acquired results outperform the specified edge detection networks arXiv:2411.14868

笔记： 这篇论文短小，架构可以借鉴。 canny算法改良的关键步骤包括： 1. 梯度值计算算子改进 2. 自动化高低阈值设定或者边缘连接算法改进

Edge Connection Based Canny Edge Detection Algorithm1 改良Canny算法，这篇论文也非常适合参考学习。

Song, Renjie; Zhang, Ziqi; Liu, Haiyang (2017). Edge connection based Canny edge detection algorithm. Pattern Recognition and Image Analysis, 27(4), 740–747. doi:10.1134/S1054661817040162

An Improved Canny Edge Detection Algorithm http://sci.bban.top/pdf/10.1109/icma.2014.6885761.pdf#view=FitH 提出一个新的soble卷积核

所有 sobel-like卷积核都属于高斯函数偏导数离散近似。

为满足不同尺度检测需求，canny算法通常需要首先进行高斯平滑。 高斯平滑 * 梯度算子等同于一个大高斯核的高斯函数偏导数。 所以引出的问题： 1. 如何自适应高斯核参数 2. 高斯金字塔

1. 1. canny算法改进

1. 高斯平滑和边缘梯度梯度检测可以合并

高斯平滑*微分=高斯函数偏导数

2. 非极大值抑制

3.多尺度合并 采用不同高斯函数偏导数监测边缘梯度并进行非极大值抑制 合并多尺度边缘 小波函数 多高斯函数+canny LoG、DoG也具备多尺度能力

但是虹膜边缘不需要多次度，而是特定尺度敏感

4. 边缘跟踪 强边缘即大尺度边缘作为种子扩展到小尺度边缘 孤立的没有和强边缘连接的被抑制

1. 1. 思考和学习

拉普拉斯函数是二次导数，和一次导数的梯度强度存在不同。

canny算法首先高斯函数再sobel算子可能带来误差。 试验：

高斯函数偏导数 高斯函数+sobel算子