  Cameras in modern devices such as smartphones, satellites and medical
equipment are capable of capturing very high resolution images and videos. Such
high-resolution data often need to be processed by deep learning models for
cancer detection, automated road navigation, weather prediction, surveillance,
optimizing agricultural processes and many other applications. Using
high-resolution images and videos as direct inputs for deep learning models
creates many challenges due to their high number of parameters, computation
cost, inference latency and GPU memory consumption. Simple approaches such as
resizing the images to a lower resolution are common in the literature,
however, they typically significantly decrease accuracy. Several works in the
literature propose better alternatives in order to deal with the challenges of
high-resolution data and improve accuracy and speed while complying with
hardware limitations and time restrictions. This survey describes such
efficient high-resolution deep learning methods, summarizes real-world
applications of high-resolution deep learning, and provides comprehensive
information about available high-resolution datasets.
