Lightweight spatial-channel adaptive coordination of multilevel refinement enhancement network for image reconstruction

Abstract

Benefiting from vigorous development of the deep learning, many CNN-based image super-resolution methods have been appearing and achieve better results than traditional algorithms. However, it is difficult for most algorithms to adaptively adjust spatial region and channel features at the same time, let alone the information exchange between them. In addition, the exchange of information between attention modules is even less visible to researchers. To solve these problems, we put forward a lightweight spatial-channel adaptive coordination of multilevel refinement enhancement network(MREN). Specifically, we construct a space-channel adaptive coordination block, which enables the network to learn the spatial region and channel feature information of interest under different receptive fields. Furthermore, the information of corresponding feature processing level in the spatial part and the channel part is exchanged by jumping connection to achieve the coordination between the two. We built a bridge of communication between attention modules through a simple linear combination operation, so as to more accurately and continuously guide the network to pay attention to the information of interest. Extensive experiments on several standard test sets have shown that our MREN achieves superior performance over other advanced algorithms with a very small number of parameters and very low computational complexity.