Lensless Reflection Digital Holographic Microscope with a Fresnel-Bluestein Transform

Shin, Sanghoon; Yu, Younghun

doi:10.3938/jkps.74.98

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Digital holography (DH) [2] has the same principle as holography but uses image sensors instead of films for recording. DH has been widely applied in many applications, such as 3D image encryption [3,4], 3D image recognition [5][6][7], digital holographic reconstruction [8,9], and digital holographic microscopy (DHM) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Noise reduction method using a variance map of the phase differences in digital holographic microscopy

2022

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The phase reconstruction process in digital holographic microscopy involves a trade‐off between the phase error and the high‐spatial‐frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high‐spatial‐frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high‐spatial‐frequency components, but the phase error will increase. To solve this trade‐off, we propose the high‐variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high‐spatial‐frequency components. Our proposed method calculates the average of the high‐variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high‐spatial‐frequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages.

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Section: Introductionmentioning

confidence: 99%

Noise reduction method using a variance map of the phase differences in digital holographic microscopy

2022

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“…However, since Joseph Goodman proposed digital holography (DH) in 1967 [25], these disadvantages have been resolved and studied by more researchers. This digital holography technology is being applied to many research fields such as 3D image visualization [3,4], object recognition [ [5,6]], and digital holographic microscopy (DHM) [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Among the mentioned technologies, DHM is widely used in many applications such as microstructure analysis [16,17], microbial research [18][19][20][21], and diagnosis of diseases using cell analysis [22,23] due to capability for obtaining 3D information of micro-objects.…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Cho

Lee

2022

Sensors

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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.

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“…And by applying the Bluestein substitution, the discrete Fourier transform can be used even if the sampling intervals of input plane are different from that of output plane. [21][22][23][24] The calculation speed of MSAS is fast because it is based on a linear convolution which can be evaluated by fast Fourier transform (FFT) effectively. Then the most suitable size of the calculation window is selected so that the numerical calculation in long-distance propagation runs efficiently.…”

Section: Introductionmentioning

confidence: 99%

Modified scaling angular spectrum method for numerical simulation in long-distance propagation*

et al. 2021

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The angular method (AS) cannot be used in long-distance propagation because it produces severe numerical errors due to the sampling problem in the transfer function. Two ways can solve this problem in AS for long-distance propagation. One is zero-padding to make sure that the calculation window is wide enough, but it leads to a huge calculation burden. The other is a method called band-limited angular spectrum (BLAS), in which the transfer function is truncated and results in that the calculation accuracy decreases as the propagation distance increases. In this paper, a new method called modified scaling angular spectrum (MSAS) to solve the problem for long-distance propagation is proposed. A scaling factor is introduced in MSAS so that the sampling interval of the input plane can be adjusted arbitrarily unlike AS whose sampling interval is restricted by the detector’s pixel size. The sampling interval of the input plane is larger than the detector’s pixel size so the size of calculation window suitable for long-distance field propagation in the input plane is smaller than the size of the calculation window required by the zero-padding. Therefore, the method reduces the calculation redundancy and improves the calculation speed. The results from simulations and experiments show that MSAS has a good signal-to-noise ratio (SNR), and the calculation accuracy of MSAS is better than BLAS.

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Lensless Reflection Digital Holographic Microscope with a Fresnel-Bluestein Transform

Cited by 7 publications

References 18 publications

Noise reduction method using a variance map of the phase differences in digital holographic microscopy

Noise reduction method using a variance map of the phase differences in digital holographic microscopy

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

Modified scaling angular spectrum method for numerical simulation in long-distance propagation*

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