@article{Zhang2017, 
author = {Junlei Zhang and Dianguang Gai and Xin Zhang and Xuemei Li},
title = {Multi-example feature-constrained back-projection method for image super-resolution},
year = {2017},
journal = {Computational Visual Media},
volume = {3},
number = {1},
pages = {73-82},
keywords = {feature constraints, back-projection, super-resolution (SR)},
url = {https://www.sciopen.com/article/10.1007/s41095-016-0070-4},
doi = {10.1007/s41095-016-0070-4},
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.}
}