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基于窗式边带阵列(WiSA)的数字全息显微镜噪声滤波方法,获取物体精确三维轮廓。

Noise Filtering Method of Digital Holographic Microscopy for Obtaining an Accurate Three-Dimensional Profile of Object Using a Windowed Sideband Array (WiSA).

机构信息

Department of Computer Science and Networks, Kyushu Institute of Technology, 680-4 Kawazu, Fukuoka 820-8502, Japan.

School of ICT, Robotics, and Mechanical Engineering, Research Center for Hyper-Connected Convergence Technology, IITC, Hankyong National University, 327 Chungang-ro, Anseong 17579, Korea.

出版信息

Sensors (Basel). 2022 Jun 27;22(13):4844. doi: 10.3390/s22134844.

DOI:10.3390/s22134844
PMID:35808340
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9269282/
Abstract

In the image processing method of digital holographic microscopy (DHM), we can obtain a phase information of an object by windowing a sideband in Fourier domain and taking inverse Fourier transform. In this method, it is necessary to window a wide sideband to obtain detailed information on the object. However, since the information of the DC spectrum is widely distributed over the entire range from the center of Fourier domain, the window sideband includes not only phase information but also DC information. For this reason, research on acquiring only the phase information of an object without noise in digital holography is a challenging issue for many researchers. Therefore, in this paper, we propose the use of a windowed sideband array (WiSA) as an image processing method to obtain an accurate three-dimensional (3D) profile of an object without noise in DHM. The proposed method does not affect the neighbor pixels of the filtered pixel but removes noise while maintaining the detail of the object. Thus, a more accurate 3D profile can be obtained compared with the conventional filter. In this paper, we create an ideal comparison target i.e., microspheres for comparison, and verify the effect of the filter through additional experiments using red blood cells.

摘要

在数字全息显微镜 (DHM) 的图像处理方法中,我们可以通过在傅里叶域中对边带进行窗口化并进行逆傅里叶变换来获得物体的相位信息。在这种方法中,需要对宽边带进行窗口化,以获取有关物体的详细信息。然而,由于 DC 谱的信息广泛分布在傅里叶域的整个范围内,因此窗口边带不仅包含相位信息,还包含 DC 信息。因此,研究如何在数字全息术中获取没有噪声的物体的相位信息是许多研究人员面临的一个挑战问题。因此,在本文中,我们提出使用窗口化边带阵列 (WiSA) 作为图像处理方法,以在 DHM 中获得没有噪声的物体的精确三维 (3D) 轮廓。所提出的方法不会影响滤波像素的邻域像素,而是在保持物体细节的同时去除噪声。因此,与传统滤波器相比,可以获得更精确的 3D 轮廓。在本文中,我们创建了一个理想的比较目标,即微球,用于比较,并通过使用红细胞进行的额外实验验证了滤波器的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/158b7e740ba1/sensors-22-04844-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/d74d08c8e839/sensors-22-04844-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/feb714d62d1e/sensors-22-04844-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/5755f0f7ca8d/sensors-22-04844-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/615b2b2af4fe/sensors-22-04844-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/838dace20198/sensors-22-04844-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/4e5722550670/sensors-22-04844-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/0678e69b6f1f/sensors-22-04844-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/6a5538780b00/sensors-22-04844-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/158b7e740ba1/sensors-22-04844-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/d74d08c8e839/sensors-22-04844-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/feb714d62d1e/sensors-22-04844-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/5755f0f7ca8d/sensors-22-04844-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/615b2b2af4fe/sensors-22-04844-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/838dace20198/sensors-22-04844-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/4e5722550670/sensors-22-04844-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/0678e69b6f1f/sensors-22-04844-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/6a5538780b00/sensors-22-04844-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14dc/9269282/158b7e740ba1/sensors-22-04844-g009.jpg

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