A super-resolution network method based on hybrid attention mechanism

To address the challenge of insufficient feature utilization in super-resolution network models, we introduce a hybrid attention model. The proposed model employs an advanced hybrid attention mechanism to effectively incorporate diverse image features into the reconstruction process, thereby significantly boosting reconstruction performance. Additionally, we optimize the information interaction through a novel sliding window approach that integrates overlapping and dilated windows. This method improves inter-window information flow, expanding the model's receptive field and bolstering its capacity to harness global features. Experimental results and visual comparisons demonstrate the method's effectiveness in restoring the original structure and texture of images.