Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

This paper reviews the NTIRE 2024 challenge on image super-resolution (x4) highlighting
the solutions proposed and the outcomes obtained. The challenge involves generating …

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

Deep neural networks have enabled astonishing transformations from low-resolution (LR) to
super-resolved images. However, whether, and under what imaging conditions, such deep …

Cryo-EM maps are valuable sources of information for protein structure modeling. However,
due to the loss of contrast at high frequencies, they generally need to be post-processed to …

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Transformer-based method has demonstrated promising performance in image super-
resolution tasks, due to its long-range and global aggregation capability. However, the …