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基于 DSTTD 系统的 MRI 图像自超分辨率。

Self-Super-Resolution of an MRI Image with Assistance of the DSTTD System.

机构信息

Center for System Design, Chennai Institute of Technology, Chennai, India.

Department of Information Technology, Vignan's Foundation for Science Technology and Research, Guntur, India.

出版信息

J Healthc Eng. 2022 Nov 24;2022:3376079. doi: 10.1155/2022/3376079. eCollection 2022.

DOI:10.1155/2022/3376079
PMID:36465249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9715326/
Abstract

. In the modern world of information technology, the need for ensuring the safety of wireless transmissions while transiting through a given network is growing rapidly. The process of transmitting images via a wireless network is fraught with difficulty. There is a possibility that data may be corrupted while being transmitted, which would result in an image with low resolution. Both of these issues were investigated head-on in this research methodology using the aiding double space-time block coding (DSTTD) system and the self-super-resolution (SSR) method. . In recent times, medical image transmission over a wireless network has received a significant amount of attention, as a result of the sharing of medical images between patients and doctors. They would want to make sure that the image was sent in a risk-free and protected manner. Arnold cat map, often known as ACM, is a well-known and widely implemented method of image transmission encryption that has been in use for quite some time. At the receiver end, SSR is now being employed in order to view the transmitted medical image in the finest possible resolution. It is anticipated that in the near future, image transmission through wireless DSTTD will be technically feasible. This is performed in order to maximize the benefits that the system has to offer in terms of both spatial diversity and multiplexing as much as is possible. . The SSR approach is used in order to represent the image in a document pertaining to human resources. ACM is used so that the image may be sent in a risk-free and protected way. The adoption of a DSTTD-based architecture for wireless communication is suggested. A comparison of the results is provided, and PSNR and SSIM values are detailed towards the results and discussion of the article.

摘要

. 在现代信息技术世界中,通过给定网络传输时确保无线传输安全的需求迅速增长。通过无线网络传输图像的过程充满了困难。在传输过程中数据有可能损坏,这将导致图像分辨率降低。在这项研究方法中,使用辅助双时空分组码 (DSTTD) 系统和自超分辨率 (SSR) 方法直接解决了这两个问题。. 近年来,由于患者和医生之间共享医学图像,通过无线网络传输医学图像受到了极大的关注。他们希望确保图像以无风险和受保护的方式发送。Arnold 猫映射,通常称为 ACM,是一种广为人知且广泛实施的图像传输加密方法,已经使用了相当长的一段时间。在接收器端,SSR 现在用于以最佳分辨率查看传输的医学图像。预计在不久的将来,通过无线 DSTTD 进行图像传输在技术上是可行的。这是为了尽可能最大化系统在空间分集和复用方面提供的优势。. SSR 方法用于以人力资源相关文档的形式表示图像。使用 ACM 可以安全地发送图像。建议采用基于 DSTTD 的架构进行无线通信。提供了结果的比较,并详细说明了 PSNR 和 SSIM 值,以详细说明文章的结果和讨论。

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2
Cross-domain heterogeneous residual network for single image super-resolution.跨域异质残差网络的单图像超分辨率。
Neural Netw. 2022 May;149:84-94. doi: 10.1016/j.neunet.2022.02.008. Epub 2022 Feb 11.
3
Text Data Augmentation for Deep Learning.用于深度学习的文本数据增强
J Big Data. 2021;8(1):101. doi: 10.1186/s40537-021-00492-0. Epub 2021 Jul 19.
4
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution.用于多退化单图像超分辨率的快速稳健级联模型
IEEE Trans Image Process. 2021;30:4747-4759. doi: 10.1109/TIP.2021.3074821. Epub 2021 May 5.
5
Cascading and Enhanced Residual Networks for Accurate Single-Image Super-Resolution.用于精确单图像超分辨率的级联增强残差网络
IEEE Trans Cybern. 2021 Jan;51(1):115-125. doi: 10.1109/TCYB.2019.2952710. Epub 2020 Dec 22.
6
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images.基于欠采样图像序列的联合 MAP 配准和高分辨率图像估计。
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7
Fundamental limits of reconstruction-based superresolution algorithms under local translation.基于局部平移的重建超分辨率算法的基本限制
IEEE Trans Pattern Anal Mach Intell. 2004 Jan;26(1):83-97. doi: 10.1109/tpami.2004.1261081.