Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

For the last 15 years, super‐resolution (SR) algorithms have successfully been applied to
magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after …

Abstract The PlanetScope CubeSat constellation is providing unprecedented global
coverage, visible to near infrared, atmospherically corrected, 3 m imagery. The revisit …

Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image
Super-resolution (SR), representing an exceptional opportunity for the remote sensing field …

In this paper, we intend to implement a class of fractional differential masks with high-
precision. Thanks to two commonly used definitions of fractional differential for what are …

Remote sensing image super-resolution (SR) plays an important role by supplementing the
lack of original high-resolution (HR) images in the study scenarios of large spatial areas or …

Resolution is a comprehensive reflection and evaluation index for the visual quality of
remote sensing images. Super-resolution processing has been widely applied for extracting …

Novel automated photogrammetry is based on four innovations. First is the cost-free
increase of overlap between images when sensing digitally. Second is an improved …

We introduce a class of inverse problem estimators computed by mixing adaptively a family
of linear estimators corresponding to different priors. Sparse mixing weights are calculated …

The need for precise (subpixel accuracy) motion estimates in conventional super-resolution
has limited its applicability to only video sequences with relatively simple motions such as …