基于深度学习的通信工程图像处理算法优化与实现OA
Optimization and Implementation of Deep Learning-based Image Processing Algorithm for Communication Engineering
本文探讨了基于深度学习技术的通信工程领域中图像处理算法的优化与实现.深度学习在图像处理领域展现出了强大的能力,但在通信工程中的具体应用尚处于发展初期.为此,本文提出了一种综合考虑通信工程需求的图像处理算法优化方法,并通过深度学习技术实现.首先,分析了通信工程中图像处理的特殊需求和挑战,然后提出了针对这些需求的优化策略,包括但不限于传输效率、抗干扰能力和实时性.接着,设计了基于深度学习的图像处理模型,并通过实验验证了其在通信工程中的性能优势.最后,在真实通信系统中部署了所提方法,并进行了性能评估与比较.实验结果表明,此方法在通信工程图像处理任务中取得了显著的优化效果,具有实用性和可行性.
This paper discusses the optimization and implementation of image processing algorithms in the field of communication engineering based on deep learning technology.Deep learning has shown a powerful ability in the field of image processing,but the specific application in communication engineering is still in the early stage of development.For this reason,this paper proposes an image processing algorithm optimization method that comprehensively considers the needs of communication engineering and is implemented by deep learning technology.First,the special needs and challenges of image processing in communication engineering are analyzed,and then an optimization strategy for these needs is proposed,including but not limited to transmission efficiency,anti-interference ability and real-time performance.Then,a deep learning-based image processing model is designed,and its performance advantages in communication engineering are experimentally verified.Finally,the proposed method is deployed in a real communication system and the performance is evaluated and compared.The experimental results show that this method achieves significant optimization results in communication engineering image processing tasks,and is practical and feasible.
徐燕玲;刘碧君;牛燕婷
西安机电信息技术研究所,西安 710065西安机电信息技术研究所,西安 710065西安机电信息技术研究所,西安 710065
电子信息工程
深度学习通信工程图像处理算法通信系统抗干扰能力
deep learningcommunication engineeringimage processingalgorithmcommunication systemanti-interference capability
《数码设计》 2024 (11)
67-69,3
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