Abstract:

Most existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate realistic training data by simulating the imaging process of digital cameras. And to remedy the information loss of the input, we develop a dual convolutional neural network to exploit the originally captured radiance information in raw images. In addition, we propose to learn a spatially-variant color transformation which helps more effective color corrections. Extensive experiments demonstrate that super-resolution with raw data helps recover fine details and clear structures, and more importantly, the proposed network and data generation pipeline achieve superior results for single image super-resolution in real scenarios.

Code & Data Paper Github

Please cite our paper if you find it helpful in your research:

@inproceedings{xxy-cvpr19-rawsr,

title={Towards real scene super-resolution with raw images},

author={ Xu, Xiangyu and Ma, Yongrui and Sun, Wenxiu},

booktitle={CVPR},

year={2019}

}