Roadmap on computational methods in optical imaging and holography [invited]

Applied Physics B, Год журнала: 2024, Номер 130(9)

Опубликована: Авг. 29, 2024

Computational methods have been established as cornerstones in optical imaging and holography recent years. Every year, the dependence of on computational is increasing significantly to extent that components are being completely efficiently replaced with at low cost. This roadmap reviews current scenario four major areas namely incoherent digital holography, quantitative phase imaging, through scattering layers, super-resolution imaging. In addition registering perspectives modern-day architects above research areas, also reports some latest studies topic. codes pseudocodes presented for a plug-and-play fashion readers not only read understand but practice algorithms their data. We believe this will be valuable tool analyzing trends predict prepare future holography.

Язык: Английский

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Vortex Beams and Deep Learning for Optical Wireless Communication Through Turbulent and Diffuse Media

Journal of Lightwave Technology, Год журнала: 2024, Номер 42(10), С. 3631 - 3641

Опубликована: Фев. 5, 2024

Interest in optical wireless communications (OWC) as a possible complement to RF technology has increased recently. However, the propagation of beams through atmosphere distorts beam's amplitude and phase, resulting information loss significant noise. Arising due combination multiple absorption scattering events, distortion beam makes communication difficult. In some cases, orbital angular momentum light (OAM), along with various deep learning algorithms (DL), could be helpful mitigate problem provide high-capacity links. this work, we propagate Laguerre-Gaussian (LG) different topological charges (l) under diffuse turbulent conditions develop deep-learning classification network characterize 'l' LG beam. The proposed method is later implemented using laboratory setup demonstrating on table. results show that algorithm can identify modes high accuracy even when propagates highly media. To demonstrate robustness OWC system, small grayscale images are transmitted over channel. A bit error rate (BER) only 2.3 × 10 ^-4 9.7 for tabletop experiment simulations, respectively. demonstrated low BER system suggests promising applications secure reliable data transmission adverse atmospheric conditions, highlighting potential advancing technologies.

Язык: Английский

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Reconstruction of infrared absorption and scattering coefficient distributions of semitransparent medium using Adam algorithm combined with adjoint model

Infrared Physics & Technology, Год журнала: 2024, Номер 141, С. 105481 - 105481

Опубликована: Авг. 8, 2024

Язык: Английский

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Advancing Image Reconstruction in Diffuse Optical Tomography: An Overview of Regularization Methods

Sensing and Imaging, Год журнала: 2025, Номер 26(1)

Опубликована: Май 14, 2025

Язык: Английский

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Propagation of Laguerre-Gaussian beam intensities through optically thick turbid media

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Июнь 5, 2025

Язык: Английский

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Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

Bioengineering, Год журнала: 2023, Номер 10(3), С. 382 - 382

Опубликована: Март 21, 2023

Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due the complexity of scattered light and limitations traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions have low imaging accuracy, shallow depth, long computation time, high signal-to-noise ratio. However, machine learning potentially improve performance DOT being better equipped solve inverse problems, perform regression, classify medical images, reconstruct biomedical images. In this study, we utilized model called "XGBoost" detect tumors in inhomogeneous breasts applied post-processing technique based on genetic programming accuracy. The proposed algorithm was tested using simulated measurements from complex evaluated cosine similarity metrics root mean square error loss. results showed that use XGBoost could lead more accurate detection compared methods, with reconstructed having an average than 0.97 ± 0.07 around 0.1270 0.0031 ground truth.

Язык: Английский

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Optimal Image Reconstruction and Anomaly Detection in Diffuse Optical Tomography with Hybrid CNN-LSTM

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Сен. 20, 2024

Язык: Английский

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Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

Journal of Biomedical Optics, Год журнала: 2023, Номер 28(02)

Опубликована: Фев. 6, 2023

SignificanceThe machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently.AimThis research aims develop an end-to-end deep framework for diffuse (DOI) with multiple datasets detect breast cancer reconstruct its properties the early stages.ApproachThe proposed Periodic-net is nondestructive (DL) algorithm reconstruction evaluation of inhomogeneities inverse model high accuracy, while boundary measurements are calculated by solving forward problem sources/detectors arranged uniformly around circular domain various combinations, 16 × 15, 20 19, 36 35 measurement setups.ResultsThe results image on numerical phantom demonstrate that network provides higher-quality images greater amount small details, superior immunity noise, sharper edges reduction artifacts than other state-of-the-art competitors.ConclusionsThe highly effective at simultaneous properties, i.e., absorption reduced scattering coefficients, optimizing time without degrading inclusions localization quality.

Язык: Английский

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Rapid extraction of skin physiological parameters from hyperspectral images using machine learning

Applied Intelligence, Год журнала: 2022, Номер 53(13), С. 16519 - 16539

Опубликована: Дек. 10, 2022

Abstract Noninvasive assessment of skin structure using hyperspectral images has been intensively studied in recent years. Due to the high computational cost classical methods, such as inverse Monte Carlo (IMC), much research done with aim machine learning (ML) methods reduce time required for estimating parameters. This study aims evaluate accuracy and estimation speed ML this purpose compare them traditionally used adding-doubling (IAD) algorithm. We trained three models – an artificial neural network (ANN), a 1D convolutional (CNN), random forests (RF) model predict seven The were on simulated data computed To improve predictive performance, we introduced stacked dynamic weighting (SDW) combining predictions all individually models. SDW was by only handful real-world spectra top ANN, CNN RF that data. Models evaluated based estimated parameters’ mean absolute error (MAE), considering surface inclination angle comparing fitted IAD On data, lowest MAE achieved (0.0030), while vivo measured (0.0113). shortest estimate parameters single spectrum 93.05 μ s. Results suggest algorithms can produce accurate estimates human optical near real-time.

Язык: Английский

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Real-time and accurate estimation ex vivo of four basic optical properties from thin tissue based on a cascade forward neural network

Biomedical Optics Express, Год журнала: 2023, Номер 14(4), С. 1818 - 1818

Опубликована: Март 23, 2023

Double integrating sphere measurements obtained from thin ex vivo tissues provides more spectral information and hence allows full estimation of all basic optical properties (OPs) theoretically. However, the ill-conditioned nature OP determination increases excessively with reduction in tissue thickness. Therefore, it is crucial to develop a model for that robust noise. Herein, we present deep learning solution precisely extract four OPs real-time tissues, leveraging dedicated cascade forward neural network (CFNN) each an additional introduced input refractive index cuvette holder. The results show CFNN-based enables accurate fast evaluation OPs, as well robustness Our proposed method overcomes highly restriction can distinguish effects slight changes measurable quantities without any priori knowledge.

Язык: Английский

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