Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of sub-image patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good trade-off between performance and complexity. The code is available at \url{https://github.com/hanlinwu/SalDRN}.
翻译:深层次的学习算法大大提高了遥感图像(RSI)超分辨率(SR)的性能。然而,网络深度和参数的提高导致计算和存储的巨大负担。直接减少现有模型的深度或宽度导致性能大幅下降。我们观察到,不同区域在RESI中的深度或广度困难有很大的性能下降。我们观察到,不同区域在RESI中的显著性能差异很大,现有方法使用同样的深度网络处理各区域的图像,造成计算机资源的浪费。此外,现有的SR方法一般先于N级整级缩缩放系数,无法在SARDRN之间执行无分级的SR,即单一模型可以处理任何潜在的规模因素。对每个规模的模型进行再培训,会浪费大量的计算资源和模型存储空间模型的空间空间空间空间空间空间空间空间空间空间空间空间。为了解决上述问题,我们提出一个显著的动态动态轮廓网络(SalDRN), 将视觉显性色度作为区域级的SR困难指标,并将一个轻度的显性突出的显性精度探测器纳入SalDRN,然后,我们设计一个显著的内深层的内定的内定的性路路路,我们选择一个动态的SDRDRSDRS的精度的精度的精度的精度的精度的精度的精度选择一个稳定的细的精度选择一个新的精度路段。