The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …

The lack of adequate stereo coverage and where available, lengthy processing time, various
artefacts, and unsatisfactory quality and complexity of automating the selection of the best …

The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars
Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution …

Recently, proposals have been made to use deep learning for hologram calculations to
directly infer holograms from three-dimensional (3D) data. However, this approach is …

Monocular depth estimation is a hot research topic in autonomous car driving. Deep
convolution neural networks (DCNN) comprising encoder and decoder with transfer learning …

We propose using coupled deep learning based super-resolution restoration (SRR) and
single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel …

Digital elevation models (DEMs) provide fundamental data for scientific and engineering
applications in lunar exploration missions. Lunar DEMs have been mainly generated by …

Millimeter-scale multi-cellular level imagers enable various applications, ranging from
intraoperative surgical navigation to implantable sensors. However, the tradeoffs for …

A novel depth estimation technique based on a single close-up image is proposed in this
paper for better understanding of the geometry of an unknown scene. Previous works focus …

This paper presents an alternative method to the classical yet foundational problem of
recovering the geometry of a 3D object from a single 2D brightness image, ie, shape-from …