Abstract:
In recent year, the convolution neural networks(CNNs) have been becoming a very important machine learning approach for computer vision and have made great progress in single image super-resolution(SISR). This paper propose a two-channel convolutional neural network for super-resolution(SR) problem. The shallow channel reconstructs the overall profile of the image and retains the original image information. The deep channel recovers the high-frequency texture feature of the image. Moreover, densely connected convolutional networks are introduced in deep channel to more efficiently recover the high-frequency information of image. In a typical feedforward CNN, some of high-frequency information may be lost in latter convolutional layers. The proposed dense connections with the previous convolutional layers can compensate for the loss and further strengthen the high-frequency information. Moreover, the proposed network connects each layer to other layer in a feedforward way so that each layer can obtain a feature map from all previous layers and map its own feature to all subsequent layers. To a certain extent, this may slow down the gradient dissipation in the training process. Different from the densely connected convolution networks in object recognition, we combine features through summation instead of concatenating them in order to better adapt the SR problem. Simultaneously, the fusion layers are used to synthesize the outputs of the two individual networks by adding additional convolution layers. Each individual network obtains a mapping from low resolution(LR) to SR space. Since the output of the two individual networks may have different context features, additional convolution layers are applied on these feature maps. By considering the difference of the structures of two networks, we add a convolutional layer after the sum of the two networks, taking the tanh as activation function. It is shown from simulation results that the proposed model can attain better performance in the subjective and objective aspects, compared with the state-of-the arts in most circumstances.