Deep Feature Aggregation for Lightweight Single Image Super-Resolution

In recent years, a number of lightweight single-image super-resolution (SISR) network methods heave been proposed. However, most existing approaches do not make full use of the information before and after the convolution and the high-frequency information of the image. In this paper, we propose a lightweight deep feature aggregation network (DFAnet), which fuses the outputs of all the deep feature aggregation blocks (DFAB) through the designed nonlinear global feature fusion (NGFF) module. The DFAB includes deep feature aggregation structure (DFAS) and non-local sparse attention mechanism (NLSA), where DFAS consists of several aggregation convolutions and information rearrangement operations. Then the output of DFAS is assessed by non-local sparse attention module to form our basic block DFAB. Furthermore, we design a nonlinear global feature fusion (NGFF) module to learn the nonlinear relationship between the output of each DFAB, which encourages every DFAB to pay attention to different patterns of the image. The qualitative and quantitative experimental results on several benchmark datasets show the proposed method achieves the state-of-the-art results in term of reconstruction accuracy, computational complexity and memory consumption.