Abstract
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 noninteger 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 MetaUpscale 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.