Computational neuromorphic imaging: principles and applications

Published: March 13, 2024

The widespread presence and use of visual data highlight the fact that conventional frame-based electronic sensors may not be well-suited for specific situations. For instance, in many biomedical applications, there is a need to image dynamic specimens at high speeds, even though these objects occupy only small fraction pixels within entire field view. Consequently, despite capturing them frame rate, resulting pixel values are uninformative therefore discarded during subsequent computations. Neuromorphic imaging, which makes an event sensor responds changes intensities, ideally suitable detecting such fast-moving objects. In this work, we outline principle detectors, demonstrate their computational imaging setting, discuss algorithms process variety applications.

Language: Английский

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Superimposed multi-harmonic interference frequency and phase measurements based on non-synchronous sampling quantization and all-phase spectrum correction

Measurement, Journal Year: 2024, Volume and Issue: 236, P. 115114 - 115114

Published: June 13, 2024

Language: Английский

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Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective

IEEE Transactions on Signal Processing, Journal Year: 2024, Volume and Issue: 72, P. 3303 - 3317

Published: Jan. 1, 2024

Language: Английский

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A generative approach for lensless imaging in low-light conditions

Optics Express, Journal Year: 2025, Volume and Issue: 33(2), P. 3021 - 3021

Published: Jan. 7, 2025

Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of focusing lens and limited lighting such environments often results low-light conditions, where measurements suffer from complex noise interference due insufficient capture photons. This study presents robust reconstruction method high-quality scenarios, employing two complementary perspectives: model-driven data-driven. First, we apply physics-model-driven perspective reconstruct range space pseudo-inverse measurement model—as first guidance extract information noisy measurements. Then, integrate generative-model-based suppress residual noises—as second noises initial results. Specifically, learnable Wiener filter-based module generates an initial, reconstruction. fast and, more importantly, stable generation clear image version, implement modified conditional generative diffusion module. converts raw into latent wavelet domain efficiency uses bidirectional training processes stabilization. Simulations real-world experiments demonstrate substantial improvements overall visual quality, advancing lensless challenging

Language: Английский

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Unsupervised crosstalk suppression for self-interference digital holography

Optics Letters, Journal Year: 2025, Volume and Issue: 50(4), P. 1261 - 1261

Published: Jan. 22, 2025

Self-interference digital holography extends the application of to non-coherent imaging fields such as fluorescence and scattered light, providing a new solution, best our knowledge, for wide field 3D low coherence or partially coherent signals. However, cross talk information has always been an important factor limiting resolution this method. The suppression is complex nonlinear problem, deep learning can easily obtain its corresponding model through data-driven methods. in real experiments, it difficult paired datasets complete training. Here, we propose unsupervised method based on cycle-consistent generative adversarial network (CycleGAN) self-interference holography. Through introduction saliency constraint, model, named crosstalk suppressing with neural (CS-UNN), learn mapping between two image domains without requiring training data while avoiding distortions content. Experimental analysis shown that suppress reconstructed images need strategies large number datasets, effective solution technology.

Language: Английский

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Estimating object and field phase through in-line intensity measurements using a twinning algorithm

Optics Communications, Journal Year: 2025, Volume and Issue: unknown, P. 131544 - 131544

Published: Jan. 1, 2025

Language: Английский

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Diagnosis of focal spots at relativistic intensity utilizing coherent radiation from laser-driven flying electron sheets

Matter and Radiation at Extremes, Journal Year: 2025, Volume and Issue: 10(2)

Published: Feb. 10, 2025

Experimental validation of laser intensity is particularly important for the study fundamental physics at extremely high intensities. However, reliable diagnosis focal spot and peak faces huge challenges. In this work, we demonstrate first time that coherent radiation farfield patterns from laser–foil interactions can serve as an in situ, real-time, easy-to-implement diagnostic ultraintense focus. The laser-driven electron sheets, curved by spatially varying field leaving targets nearly speed light, produce doughnut-shaped depending on shapes absolute Assisted particle-in-cell simulations, achieve measurements spot, provide immediate feedback to optimize spots intensity.

Language: Английский

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Advancements in ultrafast photonics: confluence of nonlinear optics and intelligent strategies

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Feb. 25, 2025

Abstract Automatic mode-locking techniques, the integration of intelligent technologies with nonlinear optics offers promise on-demand control, potentially overcoming inherent limitations traditional ultrafast pulse generation that have predominantly suffered from instability and suboptimality open-loop manual tuning. The advancements in algorithm-driven automatic techniques primarily are explored this review, which also revisits fundamental principles optical absorption, examines evolution categorization conventional techniques. convergence interactions has intricately expanded scope photonics, unveiling considerable potential for innovation catalyzing new waves research breakthroughs photonics characters.

Language: Английский

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Training networks without wavefront label for pixel-based wavefront sensing

Frontiers in Physics, Journal Year: 2025, Volume and Issue: 13

Published: March 4, 2025

Traditional image-based wavefront sensing often faces challenges in efficiency and stagnation. Deep learning methods, when properly trained, offer superior robustness performance. However, obtaining sufficient real labeled data remains a significant challenge. Existing self-supervised methods based on Zernike coefficients struggle to resolve high-frequency phase components. To solve this problem, paper proposes pixel-based method for deep sensing. This predicts the aberration pixel dimensions preserves more information while ensuring continuity by adding constraints. Experiments show that network can accurately predict dataset, with root mean square error of 0.017λ. resulting higher detection accuracy compared predicting coefficients. work contributes application high-precision practical conditions.

Language: Английский

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Introduction

Published: Jan. 1, 2025

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