改进边缘检测算法及其FPGA实现

Improved edge detection algorithm and its FPGA implementation

针对传统边缘检测算法在FPGA上实现存在缺乏自适应性及实时性差的问题,结合FPGA高速并行处理数据的特点,提出一种改进的边缘检测算法。该算法首先将双边滤波应用于Canny边缘检测算法,在降低噪声的同时保留图像边缘梯度信息;其次使用改进的Sobel算子,在4个方向上进行梯度幅值计算,以提高图像边缘的定位精度;最后提出一种基于梯度直方图的一阶导数变化的自适应阈值选取策略,以提高算法的自适应能力。实验结果表明双边滤波处理后,图像的峰值信噪比(PSNR)相较传统Canny算法提升了109%。边缘图像的单边缘响应和边缘连接性有明显提升,同时满足实时性要求。为Canny算法应用到嵌入式机器视觉中提供一种可行方案。

Aiming at the traditional edge detection algorithm implemented on FPGA,there are problems of lack of adaptivity and poor real-time performance,combined with the characteristics of high-speed parallel processing data on FPGA,an improved edge detection algorithm is proposed.The algorithm firstly applies bilateral filtering to the Canny edge detection algorithm,which retains the gradient information of the image edge while reducing the noise;secondly,it uses the improved Sobel operator to calculate the gradient magnitude in four directions to improve the localization accuracy of the image edges;finally,it proposes an adaptive threshold selection strategy based on the change of the first-order derivatives of the gradient histogram in order to improve the adaptive capability of the algorithm.The experimental results show that the peak signal-to-noise ratio(PSNR)of the image after the bilateral filtering process is improved by 109%compared with the traditional Canny algorithm,and the single edge response and connectivity of the edges are significantly improved,while meeting the real-time requirements.Provide a feasible solution for the application of Canny algorithm to embedded machine vision.