Noise Filtering Method of a 3D Target Image Based on Machine Learning
In order to effectively improve the quality of three-dimensional target images, this study designed a method for the noise filtering of three-dimensional target images based on machine learning. Firstly, the 3D target image is represented digitally. On this basis, the image data structure, which is convenient for computer processing, is formed through the process of sampling and quantification. Then, additive white gaussian noise and random value impulse noise are combined to form mixed noise, and the model of mixed noise is established. Through the detection process and denoising process, combined with machine learning, the noise of the 3D target image is filtered out. The experimental results show that the PSNR and SSIM values of 3D target images are ideal after the processing of the filter method based on machine learning, and the image texture complexity value is high, which fully demonstrates the effectiveness of this method.
Keywords: Machine learning, Three-dimensional target image, Mixed noise, Filtering, Digital
Y. Ren, "Noise Filtering Method of a 3D Target Image Based on Machine Learning", Engineering
Intelligent Systems, vol. 29 no. 4, pp. 195-204, 2021.