Super-resolution (SR) is a long-standing problem in image processing and computer vision
and has attracted great attention from researchers over the decades. The main concept of …

In recent years, the development of super-resolution (SR) algorithms based on convolutional
neural networks has become an important topic in enhancing the resolution of multi-channel …

A new approach to evaluating the informativity of a training sample when recognizing
images obtained by means of remote sensing is proposed. It is shown that the informativity …

Super-resolution of satellite imagery poses unique challenges. We propose a hybrid method
comprising two existing deep network super-resolution approaches, namely a feedforward …

The differential count of white blood cells (WBCs) can effectively provide disease information
for patients. Existing stained microscopic WBC classification usually requires complex …

The chapter proposes a new approach to estimate the quality of training datasets for
convolution neural networks with deep learning. It is shown that the accuracy of image …

Vision-based displacements can be used to measure response of structures subjected to
dynamic loading. The accuracy of these measurements can be dependent on a number of …

Deep learning has proved to be successful for many computer vision and natural language
processing applications. In this dissertation, three studies have been conducted to show the …

Despite the abundance of open Earth Observation (EO) data from the Copernicus program
and the GEOSS platform, their uptake in the context of Climate Change (CC) related …

Super-Resolution (SR) is a branch of deep learning aiming at improving the resolution of an
image preserving as much detail as possible. This technology applied to overhead imagery …