Spectral-scale Attention for Arbitrary Scale Remote Sensing Image Super-resolution
An innovative super-resolution method for remote sensing images is proposed,capable of supporting arbitrary magnification and effectively restoring spatial details of remote sensing images,thus offering higher flexibility for diverse applications.The method is based on the spectral-scale attention mechanism,a single neural network is utilized to process images at arbitrary magnifications.Network weights and depth-wise separable meta up-sample are generated dynamically,high-precision image reconstruction is realized while network parameters and computational complexity are significantly reduced.Extensive experimentation on four remote sensing image datasets indicates the approach per-forms better in both quantitative metrics and visualization quality.
remote sensing imagessuper-resolution at arbitrary magnificationdeep learningspectral-scale attentiondepth-wise separable meta up-sample
万方数据
维普
