Reduction of speckle noise in digital holography by combination of averaging several reconstructed images and modified nonlocal means filtering

Che, Leiping; Xiao, Wen; Pan, Feng; Dong, Bin; Zhong, Zheng

doi:10.1016/j.optcom.2018.05.004

Cited by 10 publications

(13 citation statements)

References 35 publications

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“…And it is also a difficult noise to deal with. [51][52][53][54] The parameters of the relevant physical quantities are set as follows: the wavelength of the light source is λ = 0.6328 μm. The distance z between the simulated object and the CCD is z = 1000 mm.…”

Section: Simulation Resultsmentioning

confidence: 99%

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Zhang,

Song,

Qiu

2023

Jpn. J. Appl. Phys.

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Accurately determining the focus plane of the reconstructed image is crucial for obtaining high-quality reconstructed images in the process of digital hologram reconstruction. In this paper, a focusing evaluation function based on S-th power weighted neighborhood correlation coefficient (SPWNCC) is proposed to realize automatic focusing of the reconstructed image in off-axis digital Fresnel holography. The Fresnel transform method is utilized as the off-axis digital holographic reconstruction algorithm. Both the numerical simulation and optical experiment results are given to verify the validity of the proposed autofocusing method. The obtained focusing curve can maintain good unimodality and noise immunity performance over a large search range (approximately 800 mm). The proposed SPWNCC based focusing evaluation function has a certain guiding significance on the automatic focusing of off axis digital holographic reconstructed images of long distance recorded objects.

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Section: Simulation Resultsmentioning

confidence: 99%

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Zhang,

Song,

Qiu

2023

Jpn. J. Appl. Phys.

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“…where k => 8000 r is the correlation coefficient. The greatest standard deviation estimate is received when r value is close to zero, then σ r ≈ 0.011 where r ≈ 0.5 we have σ r ≈ 0.007 [29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Covariance Model Of Vibration Signal Parametersmentioning

confidence: 94%

Analysis of Dynamic Parameters of a Railway Bridge

et al. 2019

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This article analyses the dispersion of vibration accelerations of a railway bridge during the passage of a train, and presents an analysis of their parameters after the application of the theory of covariance functions. The measurements of vibration accelerations at the fixed points of the beams of the overlay of the bridge were recorded in the time scale as digital arrays (matrices). The values of inter-covariance functions of the arrays of data of measurements of digital vibration accelerations and the values of auto-covariance functions of the individual arrays, changing the quantization interval in the time scale, were calculated. The compiled software Matlab 7 in the operator package environment was used in calculations. This article aims at determining the interdependencies of results of vibrations of bridge points rather than at the impact which a train makes on a bridge without emphasizing the modal parameters of the bridge. The aforementioned interdependencies make it possible to predict the results of hard-to-reach points.

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“…where γ > 0 is a descent parameter and prox γR D denotes the proximity operator of the functional R D . Here, we follow the plug-andplay prior philosophy [23][24][25] and replace prox γR D in (11) by the Algorithm 1 Proposed algorithm to solve (9) Require:…”

Section: Final Reconstruction Using the Learnt Dictionarymentioning

confidence: 99%

“…Put simply, the ODT lateral resolution is better than its axial resolution. Finally, the measured fields contain optical aberrations or speckle noise [8,9] which are usually unknown and constitute a source of model mismatch. To mitigate these problems, former works added prior knowledge in the reconstruction process using regularization [4,7,10,11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Regularization for Three-Dimensional Optical Diffraction Tomography

Pham

Soubies

Ayoub

et al. 2020

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

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Optical diffraction tomography (ODT) allows one to quantitatively measure the distribution of the refractive index of the sample. It relies on the resolution of an inverse scattering problem. Due to the limited range of views as well as optical aberrations and speckle noise, the quality of ODT reconstructions is usually better in lateral planes than in the axial direction. In this work, we propose an adaptive regularization to mitigate this issue. We first learn a dictionary from the lateral planes of an initial reconstruction that is obtained with a total-variation regularization. This dictionary is then used to enhance both the lateral and axial planes within a final reconstruction step. The proposed pipeline is validated on real data using an accurate nonlinear forward model. Comparisons with standard reconstructions are provided to show the benefit of the proposed framework.

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Reduction of speckle noise in digital holography by combination of averaging several reconstructed images and modified nonlocal means filtering

Cited by 10 publications

References 35 publications

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Analysis of Dynamic Parameters of a Railway Bridge

Adaptive Regularization for Three-Dimensional Optical Diffraction Tomography

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