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Deep learning has been widely used in super-resolution reconstruction tasks in recent years. Most of the work is based on external examples, these methods have made great progress by training mapping functions from low-resolution (LR) image patches to high-resolution (HR) image patches compared with traditional methods. There are also a few methods focus on a single image and use the internal examples to get high-resolution images. The method based on prior knowledge obtains a large number of nonlinear mapping functions through complex convolution kernels, and significantly improves the reconstruction performance of the super-resolution task. However, these external example- based methods require a large number of patch pairs to train network parameters. Besides, most of the LR images are down-sampled from the ground truth images, not all the LR images in the real world come from the HR images, these images may be disturbed by noise, blurring and other factors, some LR images do not even have a corresponding ground truth image. These shortcomings make the training of the methods based on prior knowledge very time-consuming, and the reconstruction performance of specific images uncertain. Zero-short SR(ZSSR) firstly combines deep learning and internal examples together, and get satisfactory HR images at the test time. However, compared with the methods based on prior knowledge, ZSSR only uses the single image itself as the training dataset, directly learning the mapping functions between LR image patches and HR image patches does not fully display the self-similarity within the single image. In this paper, we further combine the internal mapping with deep-learning, learning internal mapping from different scales to get HR images with more fine details.
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