Abstract
Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.
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Acknowledgements
The authors would like to thank the anonymous reviewers for giving valuable suggestions that greatly improved the paper. The authors also thank other researchers who provided the code for their algorithms for comparative testing. This project was supported by the National Natural Science Foundation of China (Grant Nos. 61572292, 61332015, 61373078, and 61272430), and the National Research Foundation for the Doctoral Program of Higher Education of China (Grant No. 20110131130004).
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Junlei Zhang received his B.S. degree in computer science and technology from Shandong Jiaotong University, Jinan, China, in 2015. Currently, he is a master candidate in the School of Computer Science and Technology, Shandong University, Jinan, China. His research interests include computer graphics and image processing.
Dianguang Gai received his master of engineering degree in computer science and technology from Shandong University, Jinan, China, and is working in the Earthquake Administration of Shandong Province. His research interests include data warehousing and earthquake prediction.
Xin Zhang is a Ph.D. student in the Department of Computer Science and Technology, Shandong University, Jinan, China. She received her bachelor degree in computer science from Shandong University in 2012. Her research interests include image processing, computer graphics, geometry processing, and CAGD.
Xuemei Li received her master and doctor degrees from Shandong University, Jinan, China, in 2004 and 2010, respectively. She is currently an associate professor in the School of Computer Science and Technology, Shandong University, and a member of the GD and IV Lab. She is engaged in research on geometric modeling, CAGD, medical image processing, and information visualization.
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Zhang, J., Gai, D., Zhang, X. et al. Multi-example feature-constrained back-projection method for image super-resolution. Comp. Visual Media 3, 73–82 (2017). https://doi.org/10.1007/s41095-016-0070-4
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DOI: https://doi.org/10.1007/s41095-016-0070-4