Abstract
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.
Highlights
With the development of science and technology, the use of computers to process images has become a common method and technique, such as denoising, enhancing, and restoring images [1]
This paper proposes a deep label reconstruction based algorithm based on this manifold assumption, which uses a deep pre-trained recognition model to pre-label unlabeled samples known as Pseudo Label (PL), and uses a deep neural network model to simultaneously regularize and pre-train unlabeled samples with labeled data and label reconstructions
SIMULATION RESULTS AND ANALYSIS Based on semi-supervised learning, we abandon the traditional gradient descent algorithm and use the Adam algorithm to perform the replacement computation, choosing the multifeature extraction technique to extract image feature information at the first layer, and using the improved existing function to complete the activation of the relevant functions afterward
Summary
With the development of science and technology, the use of computers to process images has become a common method and technique, such as denoising, enhancing, and restoring images [1]. Information communication is highly developed and connected across the globe. People usually collect different kinds of data, either passively or actively [2]. Most of the data people receive is visual information and it is of great relevance to study how to better remove image noise [3]. The problem of noise pollution of images occurs due to defects or imperfections in imaging equipment or information systems [4]. The appearance of noisy images will adversely affect the subsequent image processing, including image recognition and transmission, extraction, and segmentation, etc. [6]
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