Lightweight Image Super-Resolution Based on Local Interaction of Multi-Scale Features and Global Fusion

In recent years, computer vision technology has been widely applied in various fields, making super-resolution (SR), a low-level visual task, a research hotspot. Although deep convolutional neural network has made good progress in the field of single-image super-resolution (SISR), its adaptability to real-time interactive devices that require fast response is poor due to the excessive amount of network model parameters, the long inference image time, and the complex training model. To solve this problem, we propose a lightweight image reconstruction network (MSFN) for multi-scale feature local interaction based on global connection of the local feature channel. Then, we develop a multi-scale feature interaction block (FIB) in MSFN to fully extract spatial information of different regions of the original image by using convolution layers of different scales. On this basis, we use the channel stripping operation to compress the model, and reduce the number of model parameters as much as possible on the premise of ensuring the reconstructed image quality. Finally, we test the proposed MSFN model with the benchmark datasets. The experimental results show that the MSFN model is better than the other state-of-the-art SR methods in reconstruction effect, computational complexity, and inference time.