基于自适应注意力融合特征提取网络的 图像超分辨率.

To address the issue of large image super-resolution models with excessive parameters that are difficult to deploy, as well as the poor performance of existing lightweight image super-resolution models, this paper proposed an image super-resolution model based on adaptive attention fusion feature extraction network (AAFFEN). The model consisted primarily of a large kernel attention block and multiple efficient attention fusion feature extraction blocks. Firstly, the model extracted the shallow feature information using the large kernel attention block, and then a cascaded series of efficient attention fusion feature extraction block performed deep feature extraction, enhancement, refinement, and redistribution of the aggregated operations on the extracted shallow feature information. The efficient attention fusion feature extraction block consisted of three parts, such as progressive residual feature extraction module, channel contrast-aware attention module, and channel-spatial joint attention module. The proposed network could achieve better image super-resolution performance with fewer parameters, making it an excellent lightweight image super-resolution model. By evaluating the proposed method on popular benchmark datasets and comparing it with existing methods, the results show that the proposed method has more superior performance. [ABSTRACT FROM AUTHOR]