With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

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 …

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 indispensable step for many optical imaging and metrology
techniques. The rapid development of deep learning has brought ideas to phase …

Phase unwrapping is an ill-posed classical problem in many practical applications of
significance such as 3D profiling through fringe projection, synthetic aperture radar and …

This study proposes and evaluates five deep fully convolutional networks (FCNs) for the
semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two …

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many
synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the …

Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic
aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU …

Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy
and remote sensing applications, eg, topography reconstruction and subsidence estimation …

This article proposes deep convolutional neural networks to detect and map localized, rapid
subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar …