Computational illumination microscopy has enabled imaging of a sample’s phase, spatial features beyond the diffraction limit (Fourier Ptychography), and 3D refractive index from intensity-based measurements captured on an LED array microscope. However, these methods require up to hundreds of images, limiting applications, particularly live sample imaging. Here, we demonstrate how the experimental design of a computational microscope can be optimized using data-driven methods to learn a compressed set of measurements, thereby improving the temporal resolution of the system. Specifically, we consider the image reconstruction as a physics-based network and learn the experimental design to optimize the system’s overall performance for a desired temporal resolution. Finally, we will discuss how the system’s experimental design can be learned on synthetic training data.
The goal of this work is to incorporate Convolutional Neural Networks (CNNs) into the 3D deconvolution process without training. CNNs are well suited to the problem of 2D deconvolution, however training a CNN on 3D volumes requires excessive time and impractical amounts of training data. To circumvent these problems, we use a CNN architecture as if it were a handcrafted prior, similar to the work deep image prior. Using this method, we achieve high SSIM and PSNR metrics relative to other modern techniques for deconvolving through-focus fluorescence measurements to recover a 3D volume with no training data and minimal hyperparameter tuning.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.