GedankenNet: Self-supervised learning of hologram reconstruction using physics consistency

09/17/2022
by   Luzhe Huang, et al.
5

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes employed, most of these methods depend on large-scale, diverse, and labeled training data. The acquisition and preparation of such training image datasets are often laborious and costly, also leading to biased estimation and limited generalization to new types of samples. Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types to be imaged, the self-supervised learning model was trained using a physics-consistency loss and artificial random images that are synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of various unseen biological samples, reconstructing the phase and amplitude images of different types of objects using experimentally acquired test holograms. Without access to experimental data or the knowledge of real samples of interest or their spatial features, GedankenNet's self-supervised learning achieved complex-valued image reconstructions that are consistent with the Maxwell's equations, meaning that its output inference and object solutions accurately represent the wave propagation in free-space. This self-supervised learning of image reconstruction tasks opens up new opportunities for various inverse problems in holography, microscopy and computational imaging fields.

READ FULL TEXT

page 18

page 19

page 20

page 21

page 22

research
05/17/2021

Unsupervised Deep Learning Methods for Biological Image Reconstruction

Recently, deep learning approaches have become the main research frontie...
research
07/03/2020

Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography

At X-ray beamlines of synchrotron light sources, the achievable time-res...
research
04/25/2023

Learning imaging mechanism directly from optical microscopy observations

Optical microscopy image plays an important role in scientific research ...
research
08/03/2023

Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models

Electron microscopy (EM) images exhibit anisotropic axial resolution due...
research
07/12/2023

Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging

Deep learning has transformed computational imaging, but traditional pix...
research
01/27/2022

Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network

Deep learning-based methods in computational microscopy have been shown ...
research
02/16/2022

Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning

Ultrasound (US) is widely used for clinical imaging applications thanks ...

Please sign up or login with your details

Forgot password? Click here to reset