  Convolutional neural networks (CNNs) have shown dramatic improvements in
single image super-resolution (SISR) by using large-scale external samples.
Despite their remarkable performance based on the external dataset, they cannot
exploit internal information within a specific image. Another problem is that
they are applicable only to the specific condition of data that they are
supervised. For instance, the low-resolution (LR) image should be a "bicubic"
downsampled noise-free image from a high-resolution (HR) one. To address both
issues, zero-shot super-resolution (ZSSR) has been proposed for flexible
internal learning. However, they require thousands of gradient updates, i.e.,
long inference time. In this paper, we present Meta-Transfer Learning for
Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based
on finding a generic initial parameter that is suitable for internal learning.
Thus, we can exploit both external and internal information, where one single
gradient update can yield quite considerable results. (See Figure 1). With our
method, the network can quickly adapt to a given image condition. In this
respect, our method can be applied to a large spectrum of image conditions
within a fast adaptation process.
