Adam W. M. van Eekeren

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Multiframe super-resolution (SR) reconstruction of small moving objects against a cluttered background is difficult for two reasons: a small object consists completely of “mixed” boundary pixels and the background contribution changes from frame-to-frame. We present a solution to this problem that greatly improves recognition of small moving objects under(More)
Unlike most Super-Resolution (SR) methods described in literature , which perform only SR reconstruction on the background of an image scene, we propose a framework that performs SR reconstruction simultaneously on the background and on moving objects. After registration of the background, moving objects are detected and to each moving object registration(More)
The performance of a super-resolution (SR) reconstruction method on real-world data is not easy to measure, especially as a ground-truth (GT) is often not available. In this paper, a quantitative performance measure is used, based on triangle orientation discrimination (TOD). The TOD measure, simulating a real-observer task, is capable of determining the(More)
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