Local semi-supervised regression for single-image super-resolution

Yi Tang, Xiaoli Pan, Yuan Yuan, Pingkun Yan, Luoqing Li, Xuelong Li

Abstract: In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learning local regression functions which map a low-resolution patch to a high-resolution patch. Localization strategy is generally adopted in single-image super-resolution with nearest neighbor-based algorithms. However, the poor generalization of the nearest neighbor estimation decreases the performance of such algorithms. Though the problem can be fixed by local regression algorithms, the sizes of local training sets are always too small to improve the performance of nearest neighbor-based algorithms significantly. To overcome the difficulty, the semi-supervised regression algorithm is used here. Unlike supervised regression, the information about test samples is considered in semi-supervised regression algorithms, which makes the semi-supervised regression more powerful. Noticing that numerous test patches exist, the performance of nearest neighbor-based algorithms can be further improved by employing a semi-supervised regression algorithm. Experiments verify the effectiveness of the proposed algorithm.