Boosting performance and speed of single-image super-resolution based on partitioned linear regression
Anustup Choudhury, Peter van Beek · 2016
In this paper, we show how to boost the performance and speed of the Simple Functions (SF) algorithm for single-image superresolution [1]. This method partitions the low-resolution patch space and learns linear regressors to map low-resolution to highresolution patches. We optimize the partitioning of the patch feature space by first employing dimensionality reduction and then explicitly minimizing the overall super-resolution reconstruction error during training. We also improve selection of training patches. In the super-resolution stage, we use a k-d tree for fast nearest neighbor search of partitions, and combine multiple regression models from neighboring partitions. Experimental results on benchmark data sets show improvements in both image quality and speed over SF. Also, our method outperforms state-of-the-art super-resolution methods in image quality.