@article{Ding2019, 
author = {Na Ding and Ye-Peng Liu and Lin-Wei Fan and Cai-Ming Zhang},
title = {Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation},
year = {2019},
journal = {Journal of Computer Science and Technology},
volume = {34},
number = {3},
pages = {537-549},
keywords = {lightweight database, linear regression, local-feature interpolation, super-resolution},
url = {https://www.sciopen.com/article/10.1007/s11390-019-1925-9},
doi = {10.1007/s11390-019-1925-9},
abstract = {Single image super-resolution is devoted to generating a high-resolution image from a low-resolution one, which has been a research hotspot for its significant applications. A novel method that is totally based on the single input image itself is proposed in this paper. Firstly, a local-feature based interpolation method where both edge pixel property and location information are taken into consideration is presented to obtain a better initialization. Then, a dynamic lightweight database of self-examples is built with the aid of our in-depth study on self-similarity, from which adaptive linear regressions are learned to directly map the low-resolution patch into its high-resolution version. Furthermore, a gradually upscaling strategy accompanied by iterative optimization is employed to enhance the consistency at each step. Even without any external information, extensive experimental comparisons with state-of-the-art methods on standard benchmarks demonstrate the competitive performance of the proposed scheme in both visual effect and objective evaluation.}
}