Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm

Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filt...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 24; číslo 6; s. 1950
Hlavní autoři:	Kim, Hyun-Woo, Cho, Myungjin, Lee, Min-Chul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 19.03.2024 MDPI
Témata:	Algorithms Data mining Deep learning Digital Holographic Microscopy (DHM) Fourier transforms Image processing Imaging systems Improved Denoising Diffusion Probabilistic Models (IDDPM) Methods Microscope and microscopy Microscopy Noise control noise filtering Technology application Three dimensional imaging Improved Denoising Diffusion Probabilistic Models (IDDPM) Digital Holographic Microscopy (DHM) noise filtering
ISSN:	1424-8220, 1424-8220
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Abstract	Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.
AbstractList	Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm. Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.
Audience	Academic
Author	Cho, Myungjin Kim, Hyun-Woo Lee, Min-Chul
AuthorAffiliation	1 Department of Computer Science and Networks, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Fukuoka, Japan; kim.hyunwoo547@mail.kyutech.jp 2 School of ICT, Robotics, and Mechanical Engineering, Hankyong National University, Institute of Information and Telecommunication Convergence, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea
AuthorAffiliation_xml	– name: 1 Department of Computer Science and Networks, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Fukuoka, Japan; kim.hyunwoo547@mail.kyutech.jp – name: 2 School of ICT, Robotics, and Mechanical Engineering, Hankyong National University, Institute of Information and Telecommunication Convergence, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea
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SubjectTerms	Algorithms Data mining Deep learning Digital Holographic Microscopy (DHM) Fourier transforms Image processing Imaging systems Improved Denoising Diffusion Probabilistic Models (IDDPM) Methods Microscope and microscopy Microscopy Noise control noise filtering Technology application Three dimensional imaging
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Title	Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm
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