Single-Image Super-Resolution: A Benchmark

  • Chih-Yuan Yang
  • Chao Ma
  • Ming-Hsuan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)


Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics. In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative evaluations based on conventional full-reference metrics, human subject studies are carried out to evaluate image quality based on visual perception. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms which sheds light on future research in single-image super-resolution.


Single-image super-resolution performance evaluation metrics Gaussian blur kernel width 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chih-Yuan Yang
    • 1
  • Chao Ma
    • 1
    • 2
  • Ming-Hsuan Yang
    • 1
  1. 1.University of California at MercedUSA
  2. 2.Shanghai Jiao Tong UniversityChina

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