FLASHμ: Fast Localizing And Sizing of Holographic Microparticles

• URL: http://arxiv.org/abs/2503.11538v1
• Date: Fri, 14 Mar 2025 16:04:10 GMT
• Title: FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
• Authors: Ayush Paliwal, Oliver Schlenczek, Birte Thiede, Manuel Santos Pereira, Katja Stieger, Eberhard Bodenschatz, Gholamhossein Bagheri, Alexander Ecker,
• Abstract summary: We present a two-stage neural network architecture, FLASH$mu$, to detect small particles from holograms with large sample depths up to 20cm.<n>Our method reliably detects particles of at least 9$mu$m diameter in real holograms, comparable to the standard reconstruction-based approaches.<n>In addition to introducing a novel approach to a non-local object detection or signal demixing problem, our work could enable low-cost, real-time holographic imaging setups.
• Score: 34.82692226532414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing the 3D location and size of microparticles from diffraction images - holograms - is a computationally expensive inverse problem that has traditionally been solved using physics-based reconstruction methods. More recently, researchers have used machine learning methods to speed up the process. However, for small particles in large sample volumes the performance of these methods falls short of standard physics-based reconstruction methods. Here we designed a two-stage neural network architecture, FLASH$\mu$, to detect small particles (6-100$\mu$m) from holograms with large sample depths up to 20cm. Trained only on synthetic data with added physical noise, our method reliably detects particles of at least 9$\mu$m diameter in real holograms, comparable to the standard reconstruction-based approaches while operating on smaller crops, at quarter of the original resolution and providing roughly a 600-fold speedup. In addition to introducing a novel approach to a non-local object detection or signal demixing problem, our work could enable low-cost, real-time holographic imaging setups.

Related papers

• OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope [5.835347176172883]
  We propose a novel reconstruction approach that integrates deep learning with the physical principles of off-axis holography. Our off-axis hologram network (OAH-Net) retrieves phase and amplitude images with errors that fall within the measurement error range attributable to hardware. This capability further expands off-axis holography's applications in both biological and medical studies.
  arXiv  Detail & Related papers  (2024-10-17T14:25:18Z)
• EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction [53.28220984270622]
  3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time.<n>Our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets.<n>Our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second)
  arXiv  Detail & Related papers  (2023-11-16T11:30:56Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• Fast and robust single particle reconstruction in 3D fluorescence microscopy [1.0625549557437526]
  Single particle reconstruction is a powerful technique to improve the axial resolution and the degree of fluorescent labeling. We propose a single particle reconstruction method dedicated to convolutional models in 3D fluorescence microscopy. We demonstrate on synthetic approaches in terms of resolution and reconstruction error, while achieving a low computational cost.
  arXiv  Detail & Related papers  (2023-01-23T14:20:01Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes [47.187609203210705]
  In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. Here, we present a novel algorithm based on deep convolutional neural networks to reconstruct the site-resolved lattice occupation with high fidelity.
  arXiv  Detail & Related papers  (2022-12-22T18:57:27Z)
• DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holography [3.4635026053111484]
  Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. Recently, deep learning (DL) methods have been used for more accurate holographic processing. We propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-25T17:13:45Z)
• Neural network processing of holographic images [0.0]
  HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles. We present a hologram processing algorithm, HolodecML, that utilizes a neural segmentation model, GPUs, and computational parallelization.
  arXiv  Detail & Related papers  (2022-03-16T19:20:37Z)
• IFOR: Iterative Flow Minimization for Robotic Object Rearrangement [92.97142696891727]
  IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, is an end-to-end method for the problem of object rearrangement for unknown objects. We show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data.
  arXiv  Detail & Related papers  (2022-02-01T20:03:56Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.