Adam W. M. van Eekeren

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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 is(More)
Moving objects are often the most interesting parts in image sequences. When images from a camera are undersampled and the moving object is depicted small on the image plane, processing afterwards may help to improve the visibility as well as automatic recognition of the object. This paper presents an approach which performs Super-Resolution (SR)(More)
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(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 TODmeasure, simulating a real-observer task, is capable of determining the(More)
Moving objects are often the most interesting parts in image sequences. When images from a camera are undersampled and the moving object is depicted small on the image plane, processing of the image sequence afterwards may help to improve the visibility / recognition of the object. This paper addresses this subject and presents an approach which performs(More)
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