Covid-19 Images and Video Denoising Algorithms Based on Convolutional Neural Network CNNs

Z. Messali, Salsabil Saad Saoud, Amira Lamreche
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引用次数: 1

Abstract

In this paper, the most sophisticated denoising algorithms of images and video are applied and implemented. More precisely, we study and implement the video denoising algorithms "VBM3D", "VBM4D", "DVDNet" and "FastDVDnet". Much attention is given to the latest DVDNet and FastDVDNet algorithms, which are based on CNN. We carry out a detailed quantitative and qualitative comparative study between the considered algorithms. Two assessments are adapted; the first is a qualitative comparison based on the qualityof the images / videos and the second is quantitative in terms of PSNR and running time criteria. To see the direct impact of our study on the current pandemic, and to show the importance of image and video preprocessing algorithms in the field of medical imaging; we apply the considered denoising algorithms based on CNN on our built COVID- 19 dataset and TEST_PCR videos.
基于卷积神经网络cnn的Covid-19图像和视频去噪算法
本文应用并实现了最先进的图像和视频去噪算法。更具体地说,我们研究并实现了视频去噪算法“VBM3D”、“VBM4D”、“DVDNet”和“FastDVDnet”。基于CNN的最新DVDNet和FastDVDNet算法备受关注。我们对考虑的算法进行了详细的定量和定性比较研究。调整了两项评估;第一个是基于图像/视频质量的定性比较,第二个是基于PSNR和运行时间标准的定量比较。看到我们的研究对当前大流行的直接影响,并展示图像和视频预处理算法在医学成像领域的重要性;我们将考虑的基于CNN的去噪算法应用于我们构建的COVID- 19数据集和TEST_PCR视频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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