Quantitative differential phase-contrast (DPC) microscopy is a viable imaging method that provides phase images of transparent objects by using multiple intensity images. Conventional DPC methods rely on a linearized image formation model that is applicable to weakly scattering objects only, thus limiting the phase range of objects that can be accurately imaged. Additionally, these methods necessitate additional measurements and complex algorithms to correct for system aberrations. In this presentation, we introduce self-calibrated DPC microscopy using an untrained neural network (UNN-DPC) that incorporates a nonlinear image formation model and system aberration. Our method overcomes the limitations imposed by the linearized model and enables the simultaneous reconstruction of complex object information and aberrations without a training dataset.
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.