Lightweight group convolutional network for single image super-resolution

Abstract Recently, deep learning methods have demonstrated significant reconstruction performance on Single Image Super-Resolution (SISR). However, most of them demand a huge amount of computational and memory consumption and hard to be applied to real-world applications. To this end, we propose a fast Lightweight Group Convolution Network (LGCN) model for SISR to alleviate this problem. Specifically, we develop a cascaded memory group convolutional network for SISR, which cascades several Memory Group Convolutional Networks (MGCN). There are two main merits on MGCN. One is that it consists of several group convolutional layers and 1  ×  1 convolutional layers with densely connected structure. The group convolution is utilized to reduce the parameters of LGCN, and the 1  ×  1 convolution is not only used to create a linear combination of the output of group convolutional layer, but also to gather local information progressively. The other one is that it utilizes channel attention unit to model channel-wise relationships to improve performance. Experimental results on four popular datasets show that the proposed LGCN not only outperforms the state-of-the-art SISR methods, but also achieves faster speed.