Mixer-based Local Residual Network for Lightweight Image Super-resolution

Abstract

Recently, the single image super-resolution (SISR) based on deep learning algorithm has taken more attention from the research community. There are many methods that are developed to solve this task using CNNs methods. However, most of these methods need large computational resources and consume more runtime. Due to the fact that the runtime is essential for some applications, we propose a mixer-based local residual network (MLRN) for lightweight image super-resolution (SR). The idea of the MLRN model is based on mixing channel and spatial features and mixing low and high-frequency information. This is done by designing a mixer local residual block (MLRB) to be the backbone of our model. Moreover, the bilinear up-sampling is utilized to transfer and mix low-frequency information with extracted high-frequency information. Finally, the GELU activation is used in the main model, proving its efficiency for the SR task. The experimental results show the effectiveness of the model against other state-of-the-art lightweight models. Finally, we took part in the Efficient Super-Resolution 2023 Challenge and achieved good results.