  Deep convolutional neural networks (DCNN) have been widely adopted for
research on super resolution recently, however previous work focused mainly on
stacking as many layers as possible in their model, in this paper, we present a
new perspective regarding to image restoration problems that we can construct
the neural network model reflecting the physical significance of the image
restoration process, that is, embedding the a priori knowledge of image
restoration directly into the structure of our neural network model, we
employed a symmetric non-linear colorspace, the sigmoidal transfer, to replace
traditional transfers such as, sRGB, Rec.709, which are asymmetric non-linear
colorspaces, we also propose a "reuse plus patch" method to deal with super
resolution of different scaling factors, our proposed methods and model show
generally superior performance over previous work even though our model was
only roughly trained and could still be underfitting the training set.
