Transfer Learning Based on A+ for Image Super-Resolution

Example learning-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. And these SR methods have shown a great potential for many practical applications. Unfortunately, most of popular example learning-based approaches extract features from limited training images. These training images are insufficient for super resolution task. Our work is to transfer some supplemental information from other domains. Therefore, in this paper, a new algorithm Transfer Learning based on A+ (TLA) is proposed for image super-resolution task. First, we transfer supplemental information from other datasets to construct a new dictionary. Then, in sample selection, more training samples are supplemented to the basic training samples. In experiments, we seek to explore what types of images can provide more appropriate information for super-resolution task. Experimental results indicate that our approach is superior to A+ when transferring images containing similar content with original data.