多尺度双注意力的图像超分辨率重建方法

Multi-scale Dual Attention Image Super-resolution Reconstruction Method

针对当前已有的图像超分辨率重建方法存在提取的特征信息单一、特征利用率低等问题,提出一种多尺度双注意力的图像超分辨率重建方法(MSDA)。首先,该方法通过多尺度特征提取块,提取输入图像不同尺度的特征信息;其次,引入双注意力机制使网络快速关注图像高频信息区域,利用跳跃连接来减少特征信息在深层次网络递进过程中的信息丢失;最后,使用dropout层来均衡化特征通道重要性,防止网络协同适应,提升模型的泛化性。在公共测试集Set5、Set14、BSD100、Urban100、Manga109上的实验结果表明:MSDA取得了较好的效果,重建后的图像具有更多高频信息,纹理细节丰富,观感上更接近原始高分辨率图像。

Addressing the issues of limited feature information extraction and low feature utilization in existing image super resolution reconstruction methods,we propose a Multi-Scale Dual Attention(MSDA)approach.Firstly,this method employs multi-scale feature extraction blocks to capture feature information from different scales of the input image.Subsequently,a dual attention mechanism is introduced to enable the network to rapidly focus on high-frequency regions in the images,while utilizing skip connections to mitigate feature information loss during deep network propagation.Lastly,a dropout layer is employed to bal⁃ance the importance of feature channels,preventing network co-adaptation,and enhancing the model’s generalization capabil⁃ity.Experimental results on public test datasets,including Set5,Set14,BSD100,Urban100,and Manga109,demonstrate that MSDA achieves superior performance by generating images with enhanced high-frequency information,enriched texture details,and a perceptual resemblance to the original high-resolution images.