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Recently there have been significant advances in image upscaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a critical factor for real applications. This paper proposes fast super-resolution methods while making no compromise on quality. First, we support the use of sparse learned dictionaries in combination with neighbor embedding methods. In this case, the nearest neighborsdoi:10.1109/iccv.2013.241 dblp:conf/iccv/TimofteDG13 fatcat:te7gdisdejdovbvcwh46jugdum