%0 Journal Article
%T 关于图像去噪的综述及优化模型的提出<br>An Overview of Image Denoising and an Optimization Model Are Presented
%A 张阿松
%J Journal of Image and Signal Processing
%P 138-150
%@ 2325-6745
%D 2024
%I Hans Publishing
%R 10.12677/jisp.2024.132013
%X 在数字图像处理领域中，图像去噪一直是一个基础而关键的课题，尤其是随着遥感技术的发展和应用，对高质量图像的需求日益增长。对于图像去噪的方法却是多种多样的，而缺乏系统地对这些方法进行归类和分析。所以本文详细探讨了现代图像去噪技术的进展，我们将图像去噪方法分为这三大类：全变分、稀疏理论、深度学习。并系统地进行展开论述其中的优缺点，通过列举主要参考文献，并根据参考文献来给我们提供思路，并对这些参考文献仔细深入研究。总结这些参考文献的优点对于我们的启发，并提供建立优化模型的理论基础。最后我们将在本文中的各种方法下提供一些具体的参考优化模型、对于一些高光谱图像数据和条纹噪声的属性进行具体分析，为其它学者提供一些新的建立优化模型的灵感。这些参考文献中的研究成果和我们提供给读者的优化模型有助于推动遥感图像处理技术的发展具有重要意义。<br />
In the field of digital image processing, image denoising has always been a basic and key subject, especially with the development and application of remote sensing technology, the demand for high-quality images is increasing day by day. There are a variety of methods for image denoising, but there is a lack of systematic classification and analysis of these methods. Therefore, this paper discusses the progress of modern image denoising technology in detail. We divide image denoising methods into three categories: Total variational, sparse theory and deep learning. And systematically expounded the advantages and disadvantages of them, by listing the main references, and according to the references to provide us with ideas, and these references carefully in-depth study. Summarize the advantages of these references for our inspiration and provide a theoretical basis for building optimization models. Finally, we will provide some specific reference optimization models under the various methods in this paper, and analyze the attributes of some hyperspectral image data and fringe noise in detail, so as to provide some new inspiration for other scholars to build optimization models. The research results in these references and the optimization model we provide to the readers are of great significance to promote the development of remote sensing image processing technology.
%K 图像去噪，遥感图像去噪，优化模型<br>Image Denoising
%K Remote Sensing Image Denoising
%K Optimization Model
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=84105