图像边缘检测的量子算法

Quantum Algorithm for Edge Detection

边缘检测是图像处理中的基本问题之一,边缘检测的许多算法是基于经典的图像处理技术。本文将介绍基于量子力学框架下的边缘检测量子算法,及其图像特征向量技术在图像检测中的应用。量子算法基于图像经典信息的量子化,通过图像处理中各类元素与量子力学基本物理量一一对应,通过图像特征构造的系统的哈密顿量,应用量子演化技术学习图像的轮廓,从而,给出我们期望的图像轮廓。我们应用像素灰度梯度、像素灰度的二阶差分、Sobel算子卷积等方法作为特征向量来构造哈密顿量。通过一组标准图(从BSDS500公开数据集的图片)测试我们的量子算法,结果显示我们的量子算法的灵敏度比传统Canny算法提高了25%,而且具有更强的抗噪声能力。

Edge detection is one of the fundamental problems in the image processing. Many algorithms of edge detection are based on the classical image processing technology. In this paper, we introduce the quantum algorithm of edge detection based on quantum mechanics and its application in image detection. Quantum algorithm is based on the quantization of the classical information of the image and one-to-one correspondence between various elements of the image processing and the concept and variables of quantum mechanics. By constructing the Hamiltonian for the image features, the quantum evolution technology for the quantum information of images learns the profile of the image, so as to give the desired image contour. We use the techniques of the pixel gray gradient, second order difference of pixel gray, Sobel operator convolution as the feature vectors to construct Hamiltonian. Our quantum algorithms are tested by a set of standard pictures (coming from BSDS500 exposed database). The testing results show that our quantum algorithm has a 25% higher sensitivity than the traditional Canny algorithm and has a stronger denoise ability.