[S+SSPR 2020] Multiple-Image Super-Resolution Using Deep Learning and Statistical Features

Jakub Nalepa, Krzysztof Hrynczenko, Michal Kawulok

Capturing, transferring, and storing high-resolution images has become a serious issue in a wide range of fields, in which these processes are costly, time-consuming, or even infeasible. As obtaining low-resolution images may be easier in practice, enhancing their spatial resolution is currently an active research area and encompasses both single- and multiple-image super-resolution techniques. In this paper, we propose a deep learning approach for multiple-image super-resolution that is independent of the number of available low-resolution images of the scene. It is in contrast to other deep networks which are crafted to deal with input stacks of a constant size, hence are not applicable once the number of low-resolution images varies. The experiments showed that our technique not only outperforms other single- and multiple-image super-resolution algorithms but also it is lightweight and delivers instant operation, thus can be deployed in hardware-constrained environments.