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
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.
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