Adaptive optics with reflected light and deep neural networks
- URL: http://arxiv.org/abs/2004.04603v1
- Date: Thu, 9 Apr 2020 15:39:51 GMT
- Title: Adaptive optics with reflected light and deep neural networks
- Authors: Ivan Vishniakou, Johannes D. Seelig
- Abstract summary: We develop a method for adaptive optics with reflected light and deep neural networks compatible with an epi-detection configuration.
Large datasets of sample aberrations which consist of excitation and detection path aberrations as well as the corresponding reflected focus images are generated.
Deep neural networks can disentangle and independently correct excitation and detection aberrations based on reflected light images recorded from scattering samples.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Light scattering and aberrations limit optical microscopy in biological
tissue, which motivates the development of adaptive optics techniques. Here, we
develop a method for adaptive optics with reflected light and deep neural
networks compatible with an epi-detection configuration. Large datasets of
sample aberrations which consist of excitation and detection path aberrations
as well as the corresponding reflected focus images are generated. These
datasets are used for training deep neural networks. After training, these
networks can disentangle and independently correct excitation and detection
aberrations based on reflected light images recorded from scattering samples. A
similar deep learning approach is also demonstrated with scattering guide
stars. The predicted aberration corrections are validated using two photon
imaging.
Related papers
- Application of RESNET50 Convolution Neural Network for the Extraction of Optical Parameters in Scattering Media [4.934656140862609]
We train a general purpose convolutional neural network RESNET 50 with simulated data based on Monte Carlo simulations.
We show that compared with previous work our approach gives comparable or better reconstruction accuracy with training on a much smaller dataset.
arXiv Detail & Related papers (2024-04-25T14:36:00Z) - Improving Lens Flare Removal with General Purpose Pipeline and Multiple
Light Sources Recovery [69.71080926778413]
flare artifacts can affect image visual quality and downstream computer vision tasks.
Current methods do not consider automatic exposure and tone mapping in image signal processing pipeline.
We propose a solution to improve the performance of lens flare removal by revisiting the ISP and design a more reliable light sources recovery strategy.
arXiv Detail & Related papers (2023-08-31T04:58:17Z) - Comparison of convolutional neural networks for cloudy optical images
reconstruction from single or multitemporal joint SAR and optical images [0.21079694661943604]
We focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image.
We propose a simple framework that eases the creation of datasets for the training of deep nets targeting optical image reconstruction.
We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images.
arXiv Detail & Related papers (2022-04-01T13:31:23Z) - Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image
Synthesis [65.47507533905188]
Conditional generative adversarial networks have been applied to generate synthetic histopathology images.
We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
arXiv Detail & Related papers (2021-10-27T18:54:25Z) - Differentiable model-based adaptive optics for two-photon microscopy [0.0]
Aberrations limit scanning fluorescence microscopy when imaging in scattering materials such as biological tissue.
Model-based approaches for adaptive optics take advantage of a computational model of the optical setup.
We extend this approach to two-photon scanning microscopy.
arXiv Detail & Related papers (2021-04-29T12:50:08Z) - Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian
Photometric Stereo [61.6260594326246]
We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously.
Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
arXiv Detail & Related papers (2021-03-22T18:06:58Z) - Uncalibrated Neural Inverse Rendering for Photometric Stereo of General
Surfaces [103.08512487830669]
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem.
Existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both.
We propose an uncalibrated neural inverse rendering approach to this problem.
arXiv Detail & Related papers (2020-12-12T10:33:08Z) - SIR: Self-supervised Image Rectification via Seeing the Same Scene from
Multiple Different Lenses [82.56853587380168]
We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same.
We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters.
Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
arXiv Detail & Related papers (2020-11-30T08:23:25Z) - Scale-, shift- and rotation-invariant diffractive optical networks [0.0]
Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces to compute a desired statistical inference task.
Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase.
This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant.
arXiv Detail & Related papers (2020-10-24T02:18:39Z) - Differentiable model-based adaptive optics with transmitted and
reflected light [0.0]
We show that combining model-based adaptive optics with the optimization techniques of machine learning frameworks can find aberration corrections with a small number of measurements.
Focusing in transmission can be achieved based only on reflected light, compatible with an epidetection imaging configuration.
arXiv Detail & Related papers (2020-07-27T09:39:19Z) - Deep Photon Mapping [59.41146655216394]
In this paper, we develop the first deep learning-based method for particle-based rendering.
We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points.
Our network encodes individual photons into per-photon features, aggregates them in the neighborhood of a shading point, and infers a kernel function from the per-photon and photon local context features.
arXiv Detail & Related papers (2020-04-25T06:59:10Z)
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.