Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …

Deep learning-based image reconstruction methods have achieved remarkable success in
phase recovery and holographic imaging. However, the generalization of their image …

With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

Phase unwrapping is an important but challenging issue in phase measurement. Even with
the research efforts of a few decades, unfortunately, the problem remains not well solved …

Artificial intelligence (AI) techniques such as deep learning (DL) for computational imaging
usually require to experimentally collect a large set of labeled data to train a neural network …

Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …

Tomographic flow cytometry by digital holography is an emerging imaging modality capable
of collecting multiple views of moving and rotating cells with the aim of recovering their …

In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic
reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting …