Single image resolution enhancement by efficient dilated densely connected residual network

Convolution Neural Networks have been widely applied in single image super-resolution (SR). Recent works have shown the superior performance of deep networks for SR tasks. With just an increase in the model's depth, more features and parameters (which lead to high computational cost) can be practically extracted. In this paper, we leverage the ground-truth high-resolution (HR) image as a useful guide for learning and present an effective model based on progressive dilated densely connected and a novel activation function, which is appropriate for image SR problems. Different to the common per-pixel activation functions, like Sigmoids and ReLUs, the proposed activation unit has a nonlinear learnable function with some short connections. These strategies help the network to obtain deep and complex features, consequently, the network demanding a much smaller number of layers to have similar performance for image SR, which supports the exponential growth of the receptive field, parallel by increasing the filter size. The dense connectivity facilitates feature extraction in the network and residual connections facilitate feature re-use that both are required to improve the performance of the network. Based on the experimental results, the proposed model accelerates 2 times faster than the current deep network approaches; the proposed network also achieves higher SR performance as compared to state-of-the-art results.