Deep learning for fast super-resolution reconstruction from multiple images

Michal Kawulok; Pawel Benecki; Krzysztof Hrynczenko; Daniel Kostrzewa; Szymon Piechaczek; Jakub Nalepa; Bogdan Smolka

doi:10.1117/12.2519579

14 May 2019 Deep learning for fast super-resolution reconstruction from multiple images

Michal Kawulok, Pawel Benecki, Krzysztof Hrynczenko, Daniel Kostrzewa, Szymon Piechaczek, Jakub Nalepa, Bogdan Smolka

Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 109960B (2019) https://doi.org/10.1117/12.2519579
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Recent advancements in single-image super-resolution reconstruction (SRR) are attributed primarily to convolutional neural networks (CNNs), which effectively learn the relation between low and high resolution and allow for obtaining high-quality reconstruction within seconds. SRR from multiple images benefits from information fusion, which improves the reconstruction outcome compared with example-based methods. On the other hand, multiple-image SRR is computationally more demanding, mainly due to required subpixel registration of the input images. Here, we explore how to exploit CNNs in multiple-image SRR and we demonstrate that competitive reconstruction outcome can be obtained within seconds.

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Michal Kawulok, Pawel Benecki, Krzysztof Hrynczenko, Daniel Kostrzewa, Szymon Piechaczek, Jakub Nalepa, and Bogdan Smolka "Deep learning for fast super-resolution reconstruction from multiple images", Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960B (14 May 2019); https://doi.org/10.1117/12.2519579

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