Convolution and Self-Attention Fusion for Single-Image Super-Resolution Network
In recent years,super-resolution reconstruction has become a research hotspot in the field of image processing.However,super resolution reconstruction faces many challenges.When the model parameters are too large,although it can achieve good performance,it needs huge memory cost.Aiming at the problem that most image super-resolution networks can not achieve good performance and keep the network model lightweight,this paper proposes a new lightweight two-stage network for single image super-resolution.Specifically,a lightweight convolution module is designed for local feature extraction,while a lightweight Transformer module is introduced to learn long-term image dependencies for modeling global information.The experimental results show that the proposed model performs well in both objective evaluation index and visual effect.
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