Three-dimensional laser image-filtering algorithm based on multi-source information fusion and adaptive offline fog computing

Abstract

To improve the image-processing accuracy and speed of laser three-dimensional imaging system, effectively filter the noise in the image, and effectively optimize the processing speed and image accuracy, this paper proposes an improved adaptive mean-shift image-filtering algorithm based on the traditional mean-shift-filtering algorithm. First, this paper introduces the traditional mean-shift-filtering algorithm, and improves it on the basis of the traditional algorithm. Experiments show that the mean square deviation of the pixels in the area of selection can be used as a feather of image noise and as a control parameter to adjust the size of bandwidth matrix h adaptively, so as to achieve the optimization of accuracy and speed. When the mean square error of the area of selection is large, it shows that the noise is large, and h is increased, so that more pixels are involved in the mean calculation, thus greatly improving the accuracy of calculation. When the mean square error of the area of selection is small, it shows that the noise is small, and then, h is reduced appropriately. A small number of pixels are selected to participate in the mean calculation, which can improve the speed of the algorithm. According to the size of the broadband matrix h, the appropriate pixel values are selected to participate in the process of calculating the mean values, so as to improve the accuracy of the results. Finally, the improved algorithm is verified by comparative experiments. The experimental results show that the improved algorithm can effectively filter the noise in the image and improve the image clarity. Experiments show that the algorithm has good edge-preserving and denoising characteristics.