In the presence of strong reflecting surfaces, the detector in SS-OCT may saturate, leading to loss of information within affected A-scans and potentially disturbing axial artifacts in affected B-scans or volumes. In this work, we trained an image-based neural network to detect and remove such artifacts and restore the underlying structure by means of image inpainting. For this purpose, sets of paired images were generated from raw OCT spectra, with one image intact and the other suffering from simulated detector saturation. We demonstrate the effectiveness of the proposed method qualitatively and quantitatively.
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.