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几种滤波器对幻影图像的信噪比比较。

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image.

机构信息

College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 2014, Saudi Arabia.

出版信息

J Healthc Eng. 2022 Mar 26;2022:4724342. doi: 10.1155/2022/4724342. eCollection 2022.

DOI:10.1155/2022/4724342
PMID:35378936
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8976625/
Abstract

Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain. In frequency domain, the average filter is used to smooth the image and frequency domain, and Gaussian low-pass filter is applied with empirically determined cutoff frequency. This work has six major parts such as applying average filter, determining the SNR of region of interest, transforming the image in frequency domain by discrete Fourier transform, obtaining the rectangular Gaussian low-pass filter along with a cutoff frequency, multiplying them, and carrying out the inverse Fourier transform. These steps are repeated accordingly until the resulting image SNR is equal to or greater than the spatial domain SNR. In order to achieve the goal of this study, we have analyzed the proposed approach against some of complex phantom images. The performances of these filters are compared against signal-to-noise ratio.

摘要

图像去噪方法对于减少各种噪声非常重要，这些噪声要么在捕获图像时出现，要么在图像传输过程中失真。信噪比 (SNR) 是避免理论观测在实践中实现的主要障碍之一。在这项研究中，我们针对三种不同的噪声利用了各种滤波算子，包括在空间域和频率域中对伪影图像的 SNR 进行比较。在频率域中，使用平均滤波器来平滑图像和频率域，并且应用经验确定的截止频率的高斯低通滤波器。这项工作有六个主要部分，如应用平均滤波器、确定感兴趣区域的 SNR、通过离散傅里叶变换将图像转换到频域、获得带有截止频率的矩形高斯低通滤波器、将它们相乘，以及执行逆傅里叶变换。这些步骤会重复进行，直到得到的图像 SNR 等于或大于空间域 SNR。为了实现本研究的目标，我们针对一些复杂的伪影图像分析了所提出的方法。这些滤波器的性能与 SNR 进行了比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4817/8976625/fc3599d0d700/JHE2022-4724342.013.jpg
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A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.一种用于分布式声学传感数据盲去噪和波形相干增强的自监督深度学习方法。
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