Single image super-resolution by learning versus by compressive sensing (Conference Presentation)

Adrian Stern

doi:10.1117/12.2562319

23 April 2020 Single image super-resolution by learning versus by compressive sensing (Conference Presentation)

Adrian Stern

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 1139508 (2020) https://doi.org/10.1117/12.2562319
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Single image super-resolution (SR) refers to the task of obtaining a high resolution (HR) image from a low resolution (LR) image. During the last two decades, numerous techniques that infer the missing HR data from a LR image have been developed. Recently powerful deep learning algorithms have been employed for this task and achieved the state-of-the-art performance. On the other hand, during the last decade, several techniques have been developed that generate a HR image from a compressively sensed exposure. In this paper we discuss the differences between these two types of SR imaging approaches.

Conference Presentation

Citation Download Citation

Adrian Stern "Single image super-resolution by learning versus by compressive sensing (Conference Presentation)", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 1139508 (23 April 2020); https://doi.org/10.1117/12.2562319

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