  Recent research on super-resolution has achieved great success due to the
development of deep convolutional neural networks (DCNNs). However,
super-resolution of arbitrary scale factor has been ignored for a long time.
Most previous researchers regard super-resolution of different scale factors as
independent tasks. They train a specific model for each scale factor which is
inefficient in computing, and prior work only take the super-resolution of
several integer scale factors into consideration. In this work, we propose a
novel method called Meta-SR to firstly solve super-resolution of arbitrary
scale factor (including non-integer scale factors) with a single model. In our
Meta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale
module. For arbitrary scale factor, the Meta-Upscale Module dynamically
predicts the weights of the upscale filters by taking the scale factor as input
and use these weights to generate the HR image of arbitrary size. For any
low-resolution image, our Meta-SR can continuously zoom in it with arbitrary
scale factor by only using a single model. We evaluated the proposed method
through extensive experiments on widely used benchmark datasets on single image
super-resolution. The experimental results show the superiority of our
Meta-Upscale.
