基于自适应注意力融合特征提取网络的图像超分辨率

为了应对当前大型图像超分辨率模型参数过多难以部署，以及现有的轻量级图像超分辨率模型性能表现不佳的问题，提出了一种基于自适应注意力融合特征提取网络的图像超分辨率模型。该模型主要由一个大核注意力模块和多个高效注意力融合特征提取模块组成。首先，利用大核注意力模块进行浅层特征提取，然后将提取到的浅层特征信息输入级联的高效注意力融合特征提取模块进行深层特征提取、增强、细化和再分配的聚合操作。高效注意力融合特征提取模块由三个部分组成，分别是渐进式残差特征提取模块、通道对比度感知注意力模块和通道—空间联合注意力模块。该网络可以在利用少量参数的情况下实现更好的图像超分辨率性能，是一种表现优异的轻量级图像超分辨率模型。通过在流行的基准数据集上评估提出的方法，并与现有的一些方法进行对比，结果表明该方法的表现更优异。

Image super-resolution based on adaptive attention fusion feature extraction network

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: 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.