NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Eirikur Agustsson, Radu Timofte
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</a> </i> &nbsp;
This paper introduces a novel large dataset for examplebased single image super-resolution and studies the stateof-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature
more &raquo; ... and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-ofthe-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvprw.2017.150">doi:10.1109/cvprw.2017.150</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/AgustssonT17.html">dblp:conf/cvpr/AgustssonT17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hos2snnpivccrgiku3b4gnwk3q">fatcat:hos2snnpivccrgiku3b4gnwk3q</a> </span>
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