  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 SLADS- Net. 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 least- squares, 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.
