Image super-resolution reconstruction network based on expectation maximization self-attention residual

In recent years, most deep learning-based image super-resolution (SR) reconstruction methods mainly improve the quality of image reconstruction by increasing the depth of the model, while also increasing the computational cost of the model. Additionally, a lot of networks have implemented the attention mechanism to enhance their capacity for feature extraction, but it is still challenging to properly understand the properties of various regions. In response to the above problems, this paper proposes a novel SR reconstruction network based on expectation maximization (EM) self-attention residual. The network constructs a feature-enhanced residual block by improving the basic residual block to better reuse the features extracted from the residual block. In order to increase the spatial correlation of the feature information, an EM self-attention residual block is constructed by introducing the EM self-attention mechanism, which is used to enhance the feature extraction capability of each module in the deep network model. Moreover, the feature extraction structure of the entire model is constructed by cascading EM self-attention residual blocks. Finally, a reconstructed high-resolution image is obtained through an up-sampling image reconstruction module.In order to verify the effectiveness of the proposed method, this paper has carried out comparison experiments with some mainstream methods. The experimental results show that the proposed method can achieve better subjective visual effects and better objective evaluation indicators on five popular widely used SR test datasets. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.