Multi-View Acoustic Field Imaging With Digital Color Holography

Hassad, Saoucène; Ferria, Kouider; Bouamama, Larbi; Picart, Pascal

doi:10.3389/fphot.2022.929031

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…In order to keep a good spatial resolution at 1024x1024 pixels, the frame rate was voluntarily chosen less than the acoustic frequency. This requires to make adjustments of the parameters of the calculation algorithm [24]. Especially, since aliasing occurs, the acoustic frequency is observed at 2500Hz in the power spectrum density.…”

Section: Resultsmentioning

confidence: 99%

“…The localized filtering with adapted spatial bandwidth for each wavelength permits to extract the complex images along each view. Then, the temporal phase differences are calculated for each color and processed with a dedicated algorithm (not described here, see [24]) to get the amplitude and phase of the acoustic oscillation for each sight view. It follows that the set-up permits to simultaneously measure the amplitude and phase of the acoustic field along three different lines of views.…”

Section: Experimental Set-upmentioning

confidence: 99%

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Imaging acoustic field with multicolor digital holography

Picart

Hassad

2022

Holography, Diffractive Optics, and Applications XII

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This paper proposes an approach for acoustic field imaging using simultaneous multi-view digital holography based on three-color digital off-axis holography. Considering spatio-chromatic multiplexing and the recording with a monochrome sensor, the numerical processing of time-sequences of holograms yields both the amplitude and phase of the acoustic field along three different directions of observation. Experimental results with an emitter at 40kHz establish the proof-of concept of the proposed multi-view imaging for acoustic fields. Tomography imaging of the acoustic field is provided with multiple recordings when rotation the transducer.

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Section: Resultsmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

Imaging acoustic field with multicolor digital holography

Picart

Hassad

2022

Holography, Diffractive Optics, and Applications XII

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“…Currently, digital holography is gradually developing and widely implemented [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. They learned to obtain spectrograms by rotating the hologram relative to the incident radiation by a certain angle.…”

Section: Introductionmentioning

confidence: 99%

Creating humanoid intelligent digital twin on spectral and holographic approaches

Bryndin¹

2022

Res Intell Manuf Assem

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A person perceives his environment through the influence of various complexes of conjugated vibrations on the eyes, ears and other sensitive components of the body. The psyche and neural systems of a person form the impression of vibration impact. The mind creates a language equivalent and connects it with the impression that has formed. Communication links are formed between impressions and language equivalents. Live vibrational information involves a person in a communicative creative process. In the creative communicative process, human intelligence develops. The combination of modern interdisciplinary technologies can contribute to the creation of an intelligent digital twin, similar to humans on spectral and holographic principles, relying on achievements in the field of artificial intelligence. The digital twin fixes the effects of the environment in the form of a spectrogram, and stores the effect result in the form of a hologram. Multilayer neural network systems with additional training work with spectra and holograms and their communications. Language communications are associated with spectra, holograms and their communications. A humanoid digital twin with an ensemble of intellectual agents will be able to form and develop intelligence in interaction with similar digital twins and people.

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Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network

Ishikawa,

Takeuchi,

Harada

et al. 2023

Opt. Express

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This paper proposes a deep sound-field denoiser, a deep neural network (DNN) based denoising of optically measured sound-field images. Sound-field imaging using optical methods has gained considerable attention due to its ability to achieve high-spatial-resolution imaging of acoustic phenomena that conventional acoustic sensors cannot accomplish. However, the optically measured sound-field images are often heavily contaminated by noise because of the low sensitivity of optical interferometric measurements to airborne sound. Here, we propose a DNN-based sound-field denoising method. Time-varying sound-field image sequences are decomposed into harmonic complex-amplitude images by using a time-directional Fourier transform. The complex images are converted into two-channel images consisting of real and imaginary parts and denoised by a nonlinear-activation-free network. The network is trained on a sound-field dataset obtained from numerical acoustic simulations with randomized parameters. We compared the method with conventional ones, such as image filters, a spatiotemporal filter, and other DNN architectures, on numerical and experimental data. The experimental data were measured by parallel phase-shifting interferometry and holographic speckle interferometry. The proposed deep sound-field denoiser significantly outperformed the conventional methods on both the numerical and experimental data. Code is available on GitHub (https://github.com/nttcslab/deep-sound-field-denoiser).

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Multi-View Acoustic Field Imaging With Digital Color Holography

Cited by 3 publications

References 28 publications

Imaging acoustic field with multicolor digital holography

Imaging acoustic field with multicolor digital holography

Creating humanoid intelligent digital twin on spectral and holographic approaches

Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network

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