基于标准差加权高斯滤波函数及多方向Sobel算子的感兴趣区域中心子区图像期望清晰度值计算方法

Calculation Method of Expected Sharpness Value for Region of Interest in Ventral Subregion Image Based on Standard Deviation-Weighted Gaussian Filter Function and Multidirectional Sobel Operator

通过将感兴趣区域(ROI)划分为ROI中心子区、次中心子区,以及边缘子区,提出一种ROI中心子区图像期望清晰度值计算方法。将ROI水平与垂直方向划分为多个奇数ROI子区,对于不同的ROI子区采用不同标准差加权的高斯滤波函数进行滤波去噪处理,离ROI中心子区越远的ROI次中心子区与边缘子区,标准差越大,这样在保证ROI中心子区图像清晰度值的同时,又有效地降低ROI边缘子区的清晰度值,为后续计算ROI图像期望清晰度提供可靠数据。更进一步,将传统的二方向3×3 Sobel算子拓展为四方向的5×5 Sobel算子,实现更强的边缘响应以及更好的清晰度曲线。在此基础上,利用现场可编程逻辑门阵列(FPGA)高速图像处理技术实现上述算法,大大减少计算时间。实验结果表明,所提方法可以有效地消除噪声对ROI图像期望清晰度值的影响,并且能够显著减少ROI边缘子区图像的细节信息,从而始终保证ROI中心子区对焦。另外与软件计算相比,FPGA拥有更快的计算速度和更好的实时性,计算速度是软件的130倍。

A method for calculating the expected clarity value of region-of-interest(ROI)central subregion images is proposed by segmenting an ROI into ROI central,subcentral,and edge subregions.In particular,the ROI was segmented horizontally and vertically into multiple odd-numbered ROI subregions,and different standard deviation-weighted Gaussian filtering functions were used to filter and denoise different ROI subregions.The farther away from the ROI central subregion,the larger is the standard deviation between the ROI subcenter and edge subregions.This ensures the clarity value of the ROI central subregion image while effectively reduces the clarity value of the ROI edge subregion,thus providing reliable data for subsequent calculations of the expected clarity for the ROI image.Additionally,the conventional two-dimensional 3×3 Sobel operator was extended to a four-directional 5×5 Sobel operator,thus resulting in stronger edge responses and better clarity curves.Subsequently,the algorithm above was implemented using field programmable gate array(FPGA)high-speed image-processing technology,which significantly reduced the computation time.Experimental results show that the proposed method effectively eliminates the effect of noise on the expected clarity value of the ROI images and significantly reduces the details pertaining to the ROI edge-subregion images,thereby ensuring focus on the ROI center subregion continuously.Compared with software computing,FPGA presents a higher computing speed and offers better real-time performance,with a computing speed 130 times that of software computing.