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Single-Image Super-Resolution: A Benchmark*
Chih-Yuan Yang1, Chao Ma1, 2, and Ming-Hsuan Yang1
1University of California at Merced, USA
cyang35@ucmerced.edu
cma26@ucmerced.edu
mhyang@ucmerced.edu
2Shanghai Jiao Tong University, China
Abstract. 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.
Keywords: Single-image super-resolution, performance evaluation, metrics, Gaussian blur kernel width
Electronic Supplementary Material:
LNCS 8692, p. 372 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014