为了解决现有遥感图像超分辨率重建模型对长期特征相似性和多尺度特征相关性关注不足的问题,提出了一种基于跨尺度混合注意力机制的遥感图像超分辨率重建算法.首先提出了一个全局层注意力机制(global layer attention,GLA),利用层注意力机制加权融合不同层级的全局特征,建模低分辨率与高分辨率图像特征间的长期依赖关系.同时,设计了跨尺度局部注意力机制(cross-scale local attention,CSLA),在多尺度的低分辨率特征图中寻找与高分辨率图像匹配的局部信息补丁,并融合不同尺度的补丁特征,以优化模型对图像细节信息的恢复能力.最后,提出一种局部信息感知损失函数来指导图像的重建过程,进一步提高了重建图像的视觉质量和细节保留能力.在UC-Merced数据集上的实验结果表明,本文方法在 3 种放大倍数下的平均PSNR/SSIM优于大多数主流方法,并在视觉效果方面展现出更高的质量和更好的细节保留能力.
Super-resolution Reconstruction of Remote Sensing Images with Cross-scale Hybrid Attention
To address the inadequacy of existing remote sensing image super-resolution reconstruction models in long-term feature similarity and multi-scale feature relevance,this study proposes a novel remote sensing image super-resolution reconstruction algorithm based on a cross-scale hybrid attention mechanism.Initially,the study introduces a global layer attention(GLA)mechanism and employs layer-wise attention to weight and merge global features across different levels,thereby modeling the extended dependency between low-resolution and high-resolution image features.Concurrently,it designs a cross-scale local attention(CSLA)mechanism to identify and integrate local information patches in multi-scale low-resolution feature maps that correspond with high-resolution images,enhancing the model's ability to restore image details.Finally,the study proposes a local information-aware loss function to guide the image reconstruction process,further improving the visual quality and detail preservation of the reconstructed images.Experiments on UC-Merced datasets demonstrate that the proposed method outperforms most mainstream methods in terms of average PSNR/SSIM across three magnification factors and exhibits superior quality and detail preservation in visual results.
NETL
NSTL
万方数据
