Meta-SR: A Magnification-Arbitrary Network for Super-Resolution

Recent research on super-resolution has achieved greatsuccess due to the development of deep convolutional neu-ral networks (DCNNs). However, super-resolution of arbi-trary scale factor has been ignored for a long time. Mostprevious researchers regard super-resolution of differentscale factors as independent tasks. They train a specificmodel for each scale factor which is inefficient in comput-ing, and prior work only take the super-resolution of sev-eral integer scale factors into consideration. In this work,we propose a novel method called Meta-SR to firstly solvesuper-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 tradi-tional upscale module. For arbitrary scale factor, the Meta-Upscale Module dynamically predicts the weights of the up-scale filters by taking the scale factor as input and use theseweights to generate the HR image of arbitrary size. For anylow-resolution image, our Meta-SR can continuously zoomin it with arbitrary scale factor by only using a single model.We evaluated the proposed method through extensive exper-iments on widely used benchmark datasets on single imagesuper-resolution. The experimental results show the superi-ority of our Meta-Upscale.