A lightweight large receptive field network LrfSR for image super-resolution.

Deep convolutional neural networks have demonstrated excellent performance in the field of single-image super-resolution (SISR) reconstruction. However, existing methods often suffer from issues such as large number of parameters, intensive computation, and high latency, which limit the application of deep convolutional neural networks on devices with low computational resources. To solve these problems, this paper proposes a lightweight large receptive field network for image super-resolution (LrfSR). The innovations of this paper mainly include the following aspects. Firstly, we design an information distillation module based on large receptive field (LrfDM). The module achieves large receptive field by dilated convolution, and the enlarged receptive field facilitates the network to capture more pixel-to-pixel relationships and fuse multi-scale information in the feature distillation stage. This design effectively extracts the high-frequency features of the image, which can be demonstrated by the feature map. Secondly, a more efficient attention mechanism is introduced into the network, designed as ECCA and SESA, respectively, which achieves an improvement in super-resolution image quality with fewer network parameters. Experiments on Set5, Set14, B100, Urban100 and Manga109 datasets show that the LrfSR model achieves 4-fold super-resolution Peak Signal-to-Noise Ratio (PSNR) values of 32.23 dB, 28.65 dB, 27.59 dB, 26.36 dB and 30.53 dB, which is better than the existing model LKDN etc. Meanwhile, both qualitative and quantitative experimental results show that the LrfSR model explores the potential of large receptive fields in lightweight image super-resolution networks and successfully achieves a balance between high-quality image reconstruction and limited resources. The code and models are available at https://github.com/wanqin557/LrfSR .