Spectral Object Recognition in Hyperspectral Holography with Complex-Domain Denoising

Shevkunov, Igor; Katkovnik, Vladimir; Claus, Daniel; Pedrini, Giancarlo; Petrov, Nikolay V.; Егиазарян, Карен

doi:10.3390/s19235188

Cited by 23 publications

(8 citation statements)

References 16 publications

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“…where S and V are the average and variance, respectively, of the height information of the specimen; m V is the average of the variance V ; and N d is the number of phase reconstruction data for various sizes of the windowed sideband. The average of the variance m V is used as the threshold value for segmenting the high-variance pixels and the other pixels in (7). We performed an optical experiment to verify the effectiveness of HiVA.…”

Section: Hivamentioning

confidence: 99%

“…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%

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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: Hivamentioning

confidence: 99%

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 CCF algorithm is designed to deal with 3D complex-valued cube data and introduced in details in [31]. Examples of its application and features of this algorithm can be seen in [34], [44].…”

Section: Hs Complex Domain Phase Retrieval (Hsphr) Algorithmmentioning

confidence: 99%

ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging

Katkovnik¹,

Shevkunov²,

Егиазарян³

2021

Preprint

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A novel formulation of the hyperspectral broadband phase retrieval is developed for the scenario where both object and modulation phase masks are spectrally varying. The proposed algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and Spectral Proximity Operators (SPO) derived for Gaussian and Poissonian observations. Computations for these operators are reduced to the solution of sets of cubic (for Gaussian) and quadratic (for Poissonian) algebraic equations. These proximity operators resolve two problems. Firstly, the complex domain spectral components of signals are extracted from the total intensity observations calculated as sums of the signal spectral intensities. In this way, the spectral analysis of the total intensities is achieved. Secondly, the noisy observations are filtered, compromising noisy intensity observations and their predicted counterparts. The ability to resolve the hyperspectral broadband phase retrieval problem and to find the spectrum varying object are essentially defined by the spectral properties of object and image formation operators. The simulation tests demonstrate that the phase retrieval in this formulation can be successfully resolved.

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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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Spectral Object Recognition in Hyperspectral Holography with Complex-Domain Denoising

Cited by 23 publications

References 16 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

ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging

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

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