Accurate single image super-resolution using multi-path wide-activated residual network

In many recent image super-resolution (SR) methods based on convolutional neural networks (CNNs), the superior performance was achieved by training very large networks, which may not be suitable for real-world applications with limited computing resources. Therefore, it is necessary to develop more compact networks that achieve a better trade-off between the model size and the performance. In this paper, we propose an efficient and effective network called multi-path wide-activated residual network (MWRN). Firstly, as the basic building block of MWRN, the multi-path wide-activated residual block (MWRB) is presented to extract the multi-scale features. MWRB consists of three parallel wide-activated residual paths, where the dilated convolutions with different dilation factors are used to increase the receptive fields. Secondly, the fusional channel attention (FCA) module, which contains a bottleneck layer and a multi-path wide-activated residual channel attention (MWRCA) block, is designed to well exploit the multi-level features in MWRN. In each FCA, the MWRCA block refines the fused features by taking the interdependencies among feature channels into consideration. The experiments demonstrate that, compared with the state-of-the-art methods, the proposed MWRN model is able to provide very competitive performance with a relatively small number of parameters.