Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to their behavior on real images. Toward bridging this simulated-to-real gap, we introduce the Super-Resolution Erlangen (SupER) database, the first comprehensive laboratory SR database of all-real acquisitions with pixel-wise ground truth. It consists of more than 80k images of 14 scenes combining different facets: CMOS sensor noise, real sampling at four resolution levels, nine scene motion types, two photometric conditions, and lossy video coding at five levels. As such, the database exceeds existing benchmarks by an order of magnitude in quality and quantity. This paper also benchmarks 19 popular single-image and multi-frame algorithms on our data. The benchmark comprises a quantitative study by exploiting ground truth data and qualitative evaluations in a large-scale observer study. We also rigorously investigate agreements between both evaluations from a statistical perspective. One interesting result is that top-performing methods on simulated data may be surpassed by others on real data. Our insights can spur further algorithm development, and the publicy available dataset can foster future evaluations.

Activities of a clinical staff in healthcare environments must regularly be adapted to new treatment methods, medications, and technologies. This constant evolution requires the monitoring of the workflow, or the sequence of actions from actors involved in a procedure, to ensure quality of medical services. In this context, recent advances in sensing technologies, including Real-time Location Systems and Computer Vision, enable high-precision tracking of actors and equipment. The current state-of-the-art about healthcare workflow monitoring typically focuses on a single technology and does not discuss its integration with others. Such an integration can lead to better solutions to evaluate medical workflows. This study aims to fill the gap regarding the analysis of monitoring technologies with a systematic literature review about sensors for capturing the workflow of healthcare environments. Its main scientific contribution is to identify both current technologies used to track activities in a clinical environment and gaps on their combination to achieve better results. It also proposes a taxonomy to classify work regarding sensing technologies and methods. The literature review does not present proposals that combine data obtained from Real-time Location Systems and Computer Vision sensors. Further analysis shows that a multimodal analysis is more flexible and could yield better results.

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