A temporal correlation superresolution image is based on the variance of the recorded photon time trace, while its resolution is higher than that of the complementary intensity image, it is noisier. Both images, the intensity and correlation based, are fed into a deep convolutional neural network (CNN), which produces an image that is optimized to have higher resolution than the intensity image and less noise than the correlation image. The image then passes through separate linear networks that mimic the physical blurring of the imaging setup. Preliminary experimental results show similar resolution to the experimental superresolution image with less noise.
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.