Semi-Supervised Learning-Based Image Denoising for Big Data

In this paper, the research of image noise reduction based on semi-supervised learning is carried out, and the neural network is used to reduce the noise of the image, so as to achieve more stable and good image display ability. Based on the convolutional neural network algorithm, the role of activation function optimization network is studied, combined with semi-supervised learning modes such as multi-feature extraction technology, to learn and extract the key features of the input image. Semi-supervised residual learning based on convolutional network is a good image denoising and denoising network model. Compared with other excellent denoising algorithms, it has very good results. At the same time, it greatly improves the image noise pollution and makes the image details clearer. At the same time, compared with other image denoising algorithms, this algorithm can show a good peak signal-to-noise ratio under various noise standard deviations. Through the research in this article, it is verified that the improved convolutional neural network denoising model and multi-feature extraction technology have strong advantages in image denoising.