  In this paper, an efficient super-resolution (SR) method based on deep
convolutional neural network (CNN) is proposed, namely Gradual Upsampling
Network (GUN). Recent CNN based SR methods often preliminarily magnify the low
resolution (LR) input to high resolution (HR) and then reconstruct the HR
input, or directly reconstruct the LR input and then recover the HR result at
the last layer. The proposed GUN utilizes a gradual process instead of these
two commonly used frameworks. The GUN consists of an input layer, multiple
upsampling and convolutional layers, and an output layer. By means of the
gradual process, the proposed network can simplify the direct SR problem to
multistep easier upsampling tasks with very small magnification factor in each
step. Furthermore, a gradual training strategy is presented for the GUN. In the
proposed training process, an initial network can be easily trained with
edge-like samples, and then the weights are gradually tuned with more complex
samples. The GUN can recover fine and vivid results, and is easy to be trained.
The experimental results on several image sets demonstrate the effectiveness of
the proposed network.
