Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often acquired by different imaging devices/conditions, which results a vast change of underlying data distributions (called “data domains”). Moreover, due to practical labeling difficulties, some domains (e.g., devices) may...

Abstract In recent years, deep learning has brought significant progress for the problem of synthetic aperture radar (SAR) target classification. However, SAR image characteristics are highly sensitive to change imaging conditions. The inconsistency parameters (especially depression angle) leads distribution shift between training and test data severely deteriorates classification performance. ...

It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means complexity of SAR systems will increase, which affects applications images. The introduction sparse signal processing technologies into imaging proposes a new way to solve this ...