面向NEQR的基于八方向Sobel算子的量子图像边缘检测
Quantum image edge detection based on eight-direction Sobel operator for NEQR
AI总结：
针对现有量子Sobel边缘检测算法方向少、边缘细节损失的问题，提出基于八方向Sobel算子的QSED算法，设计对应量子线路，复杂度更低，能检测更多边缘尤其是对角边缘信息。
摘要：
量子Sobel边缘检测（QSED）是一类利用量子机制实现图像边缘检测的算法，可解决经典算法面临的实时性问题。但现有QSED算法仅采用二方向或四方向Sobel算子，导致部分高清图像的边缘细节信息存在一定损失。本文提出一种基于八方向Sobel算子的新型QSED算法，既能减少边缘信息损失，又能同时计算量子图像中所有像素的八个方向梯度值。此外，本文详细设计了由梯度计算、非极大值抑制、双阈值检测和边缘跟踪单元组成的具体量子线路。对于尺寸为2^n×2^n、灰度级数为q的图像，该算法的复杂度可降至O(n²+q²)，低于现有其他经典或量子算法。仿真实验表明，与二方向和四方向QSED算法相比，该算法能检测到更多边缘信息，尤其是对角边缘。
英文摘要：
Quantum Sobel edge detection (QSED) is a kind of algorithm for image edge detection using quantum mechanism, which can solve the real-time problem encountered by classical algorithms. However, the existing QSED algorithms only consider two- or four-direction Sobel operator, which leads to a certain loss of edge detail information in some high-definition images. In this paper, a novel QSED algorithm based on eight-direction Sobel operator is proposed, which not only reduces the loss of edge information, but also simultaneously calculates eight directions' gradient values of all pixel in a quantum image. In addition, the concrete quantum circuits, which consist of gradient calculation, non-maximum suppression, double threshold detection and edge tracking units, are designed in details. For a 2^n x 2^n image with q gray scale, the complexity of our algorithm can be reduced to O(n^2 + q^2), which is lower than other existing classical or quantum algorithms. And the simulation experiment demonstrates that our algorithm can detect more edge information, especially diagonal edges, than the two- and four-direction QSED algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
