Critical aspects of computational imaging systems, such as experimental design and image
priors, can be optimized through deep networks formed by the unrolled iterations of classical …

BACKGROUND Imaging technologies, which extend human vision capabilities, are such a
natural part of our current everyday experience that we often take them for granted …

Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …

Deep learning has been transforming our ability to execute advanced inference tasks using
computers. Here we introduce a physical mechanism to perform machine learning by …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Overcoming the classical diffraction limit in optical microscopy is known to be achievable by
a variety of far-field and near-field microscopy techniques. More recently, so-called micro …

Deep learning is one of today's hottest fields. This approach to machine learning is
achieving breakthrough results in some of today's highest profile applications, in …

The imaging quality of in-line digital holography is challenged by the twin-image and
aliasing effects because sensors only respond to intensity and pixels are of finite size. As a …

Recent advances in deep learning have given rise to a new paradigm of holographic image
reconstruction and phase recovery techniques with real-time performance. Through data …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …