@inproceedings{0981d3ad57fd4811852bc54ec5fa62c3,
title = "Single-image super-resolution: A benchmark",
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 = "Gaussian blur kernel width, Single-image super-resolution, metrics, performance evaluation",
author = "Yang, \{Chih Yuan\} and Chao Ma and Yang, \{Ming Hsuan\}",
year = "2014",
doi = "10.1007/978-3-319-10593-2\_25",
language = "英语",
isbn = "9783319105925",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 4",
pages = "372--386",
booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",
address = "德国",
edition = "PART 4",
note = "13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
}