An external learning assisted self-examples learning for image super-resolution

The current self-examples learning is a promising direction for image super-resolution (SR) task, but still has several main drawbacks: (i) less ability of adapting the neighboring and non-local information for self-regression function learning; (ii) less priors from the training data generated by recursively down-sampling the low resolution (LR) input. In this work, we propose an external learning assisted self-examples learning SR (Exter-SESR) framework to alleviate these issues, which is conducted in a two-stage procedure. The first part is a hybrid neural network (HNN) that takes large LR image patches as input and extracts compact features from external dataset. The learned features can offer better neighboring and non-local priors. Meanwhile, a part of HNN is able to estimate an initial high resolution (HR) image to address the second issue, since the new training data from the initial HR images does not only preserve the original prior, but also involve the extra ones from the external data. The second part is to further refine the SR output using a SESR based model. In addition, we analyze the effects of different self-examples models on the SR performance and find that Gaussian process regression (GPR) achieves superior performance. Experimental results on the benchmark show that our proposed method outperforms the existing SESR methods by a large margin in terms of both quantitative and qualitative measurements.