Practical sensorless aberration estimation for 3D microscopy with deep learning

• URL: http://arxiv.org/abs/2006.01804v2
• Date: Sun, 5 Jul 2020 19:17:05 GMT
• Title: Practical sensorless aberration estimation for 3D microscopy with deep learning
• Authors: Debayan Saha, Uwe Schmidt, Qinrong Zhang, Aurelien Barbotin, Qi Hu, Na Ji, Martin J. Booth, Martin Weigert, Eugene W. Myers
• Abstract summary: We show that neural networks trained only on simulated data yield accurate predictions for real experimental images. We also study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role.
• Score: 1.6662996732774467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise accurate results at fast processing speeds. However, collecting ground truth microscopy data for training the network is typically very difficult or even impossible thereby limiting this approach in practice. Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images. We validate our approach on simulated and experimental datasets acquired with two different microscopy modalities, and also compare the results to non-learned methods. Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role. Finally, we make our implementation freely available as open source software in Python.

Related papers

• Direct Zernike Coefficient Prediction from Point Spread Functions and Extended Images using Deep Learning [36.136619420474766]
  Existing adaptive optics systems rely on iterative search algorithm to correct for aberrations and improve images. This study demonstrates the application of convolutional neural networks to characterise the optical aberration.
  arXiv  Detail & Related papers  (2024-04-23T17:03:53Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning [0.0]
  We present and experimentally evaluate using transfer learning to address experimental data scarcity. Our approach involves pre-training the model using synthetic data generated from a less accurate analytical model. We achieve 1 dB root-mean-square error on the matrix weights implemented by a 3x3 photonic chip while using only 25% of the available data.
  arXiv  Detail & Related papers  (2023-08-10T07:33:00Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• Estimation of Optical Aberrations in 3D Microscopic Bioimages [1.588193964339148]
  We describe an extension of PhaseNet enabling its use on 3D images of biological samples. We add a Python-based restoration of images via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with the predicted PSF can not only remove the simulated aberrations but also improve the quality of the real raw microscopic images with unknown residual PSF.
  arXiv  Detail & Related papers  (2022-09-16T13:22:25Z)
• Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning [23.200130129530653]
  Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. We propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment.
  arXiv  Detail & Related papers  (2021-11-30T01:35:04Z)
• 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)
• Stereo Matching by Self-supervision of Multiscopic Vision [65.38359887232025]
  We propose a new self-supervised framework for stereo matching utilizing multiple images captured at aligned camera positions. A cross photometric loss, an uncertainty-aware mutual-supervision loss, and a new smoothness loss are introduced to optimize the network. Our model obtains better disparity maps than previous unsupervised methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-04-09T02:58:59Z)
• Self-Supervised Linear Motion Deblurring [112.75317069916579]
  Deep convolutional neural networks are state-of-the-art for image deblurring. We present a differentiable reblur model for self-supervised motion deblurring. Our experiments demonstrate that self-supervised single image deblurring is really feasible.
  arXiv  Detail & Related papers  (2020-02-10T20:15:21Z)
• Extracting dispersion curves from ambient noise correlations using deep learning [1.0237120900821557]
  We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converted to an image. We use a convolutional neural network (U-net) architecture with a supervised learning objective and incorporate transfer learning.
  arXiv  Detail & Related papers  (2020-02-05T23:41:12Z)

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.