Image denoising algorithm of social network based on multifeature fusion

Abstract

A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

“…At present, relevant scholars at home and abroad have proposed a variety of noise reduction algorithms for digital image noises and achieved good results [ 50 , 51 , 52 , 53 ], many of which can be directly applied to the noise reduction process of infrared thermal images. At present, the noise reduction methods of digital images can generally be divided into two categories: traditional noise reduction methods and deep-learning-based noise reduction methods [ 54 ].…”