SLADS-Net: Supervised learning approach for dynamic sampling using deep neural networks

Yan Zhang*, G. M.Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak

*Corresponding author for this work

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

In scanning microscopy based imaging techniques, there is a need to develop novel data acquisition schemes that can reduce the time for data acquisition and minimize sample exposure to the probing radiation. Sparse sampling schemes are ideally suited for such applications where the images can be reconstructed from a sparse set of measurements. In particular, dynamic sparse sampling based on supervised learning has shown promising results for practical applications. However, a particular drawback of such methods is that it requires training image sets with similar information content which may not always be available. In this paper, we introduce a Supervised Learning Approach for Dynamic Sampling (SLADS) algorithm that uses a deep neural network based training approach. We call this algorithm SLADSNet. We have performed simulated experiments for dynamic sampling using SLADS-Net in which the training images either have similar information content or completely different information content, when compared to the testing images. We compare the performance across various methods for training such as leastsquares, support vector regression and deep neural networks. From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar. We also discuss the development of a pre-trained SLADS-Net that uses generic images for training. Here, the neural network parameters are pre-trained so that users can directly apply SLADS-Net for imaging experiments.

Original languageEnglish (US)
Article numberS6
JournalIS and T International Symposium on Electronic Imaging Science and Technology
DOIs
StatePublished - Jan 1 2018
Event16th Computational Imaging Conference, COMIG 2018 - Burlingame, United States
Duration: Jan 28 2018Feb 1 2018

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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