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基于卷积神经网络的生态植物保护图像纹理合成

Texture synthesis of ecological plant protection image based on convolution neural network.

作者信息

Hu Libing, Zhou Fei, Fu Xianjun

机构信息

Book Information Center, Zhejiang College of Security Technology, Wenzhou, China.

Business School, Wenzhou University, Wenzhou, China.

出版信息

Front Plant Sci. 2022 Oct 18;13:1035077. doi: 10.3389/fpls.2022.1035077. eCollection 2022.

Abstract

Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years. Convolutional neural network model can learn the features in data and realize intelligent processing through the feature learning in data. Later, with the rapid improvement of convolutional neural network, texture synthesis technology based on neural network came into being. The purpose of this paper is to study the texture synthesis method of ecological plant protection image based on convolutional neural network. By studying the context and research implications, the definition of textures as well as texture synthesis methods, convolutional neural networks, and based on convolutional neural network. In the experiment, the experimental environment is established, and the subjective evaluation and objective evaluation of the image texture synthesis method experiment are investigated and studied by using swap algorithm. The experimental results show that the method used in this paper is superior to other methods.

摘要

纹理合成技术是一种重要的真实感渲染技术。纹理合成技术在图像渲染等领域也具有良好的应用前景。卷积神经网络是近年来非常流行的技术。卷积神经网络模型能够学习数据中的特征,并通过数据中的特征学习实现智能处理。后来,随着卷积神经网络的快速发展,基于神经网络的纹理合成技术应运而生。本文的目的是研究基于卷积神经网络的生态植物保护图像纹理合成方法。通过研究背景和研究意义、纹理的定义以及纹理合成方法、卷积神经网络,并基于卷积神经网络。在实验中,搭建了实验环境,并使用交换算法对图像纹理合成方法实验进行主观评价和客观评价。实验结果表明,本文所采用的方法优于其他方法。

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