Efficient Spatial Pyramid of Dilated Convolution and Bottleneck Network for Zero-Shot Super Resolution

Most CNN-based super-resolution networks require a large number of samples for model training, which may cause overfitting when trained on a specific set, and the internal self-similarity of the test image in this way has also been discarded. To resolve this problem, this paper proposes a novel zero-shot learning (ZSL) network for super resolution. In this model, the training samples are cropped from the input image, which means no extra dataset is required for model training, the overfitting problem in this way can be well avoided. Besides, since all the training samples are cropped from the input itself, the nonlocal self-similarity attributes of the test image can be fully utilized. Moreover, the efficient spatial pyramid of dilated convolutions network with bottleneck (ESP-BNet) is applied in the model as an efficient computational structure to enhance the feature representation. Comparison experiments show that the proposed approach achieves PSNR/SSIM of 31.13dB/87.9% on Set5 dataset and PSNR/SSIM of 27.62dB/78.1% on Set14 dataset, which is around 3dB better than the traditional method and about 1dB higher than those of the state-of-art method.