Application of image deblurring method and voice network analysis in 3D environment art design system

Abstract

Abstract In a non-uniform blurred scene, pixels in other places in the blurred image undergo a downgrading process, which makes it difficult to accurately estimate the blur kernel. The method based on deep learning can directly realize image deblurring without estimating the blur kernel. Therefore, in this article, we will use deep learning methods to study the problem of blind listeners in dynamic scenes. Looking at the recent deep learning methods applied to denoisers, most image deblurring processing is set to image mapping. As the many advantages of network voice communication have quickly become an important part of people's online life, more and more researchers have begun to use voice network analysis as a steganography carrier. Compared with the old steganographic carrier, the use of network voice stream as the carrier has the advantages of immediacy, higher steganographic bandwidth and variable carrier length. However, steganography based on voice network analysis is combined with certain secure communication technologies to send harmful confidential information. This may become a major security threat because it is difficult to detect. Using 3D environment visualization technology, people can directly manipulate the physical information in the 3D graphics world and communicate directly with the computer. This 3D environment visualization technology integrates the power of humans and machines in an intuitive and natural way. These innovative changes will undoubtedly significantly improve people's work efficiency. Visualization technology enables people to interact with the art design system in real time, so people can use the art design system to obtain information or use previously unimaginable ways, and then they can play their creative thinking.