Paper Info
Title
Meta-Sr: A Magnification-Arbitrary Network For Super-Resolution
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 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.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00167
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Scale factor,Integer,Pattern recognition,Convolutional neural network,Computer science,Zoom,Artificial intelligence,Magnification,Superresolution
Journal
abs/1903.00875
ISSN
Citations 
PageRank 
1063-6919
6
0.41
References 
Authors
18
6
Name
Order
Citations
PageRank
Xuecai Hu1151.94
Haoyuan Mu2181.21
Xiangyu Zhang313044437.66
Zilei Wang411013.60
Tieniu Tan511681744.35
Jian Sun625842956.90