Abstract
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 from 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.
This research was supported by the National Science Centre, Poland, under Research Grant 2019/35/B/ST6/03006, and partially by European Space Agency (DeepSent). JN was supported by the Silesian University of Technology funds (02/080/BKM20/0012).
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Notes
- 1.
In this work, we exploit the first image in the stack.
- 2.
For more details on the metrics used for evaluating SR algorithms, see [2].
- 3.
Note that we did not re-train StatNet.
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Nalepa, J., Hrynczenko, K., Kawulok, M. (2021). Multiple-Image Super-Resolution Using Deep Learning and Statistical Features. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_25
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