  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.
