Deep Blind Super-Resolution for Satellite Video

Recent efforts have witnessed remarkable progress in satellite video super-resolution (SVSR). However, most SVSR methods usually assume the degradation is fixed and known, e.g.,
bicubic
downsampling, which makes them vulnerable in real-world scenes with multiple and unknown degradations. To alleviate this issue, blind SR has, thus, become a research hotspot. Nevertheless, the existing approaches are mainly engaged in blur kernel estimation while losing sight of another critical aspect for VSR tasks: temporal compensation, especially compensating for blurry and smooth pixels with vital sharpness from severely degraded satellite videos. Therefore, this article proposes a practical blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixelwise blur levels in a coarse-to-fine manner. Specifically, we employed multiscale deformable (MSD) convolution to coarsely aggregate the temporal redundancy into adjacent frames by window-slid progressive fusion. Then, the adjacent features are finely merged into mid-feature using deformable attention (DA), which measures the blur levels of pixels and assigns more weights to the informative pixels, thus inspiring the representation of sharpness. Moreover, we devise a pyramid spatial transformation (PST) module to adjust the solution space of sharp mid-feature, resulting in flexible feature adaptation in multilevel domains. Quantitative and qualitative evaluations on both simulated and real-world satellite videos demonstrate that our BSVSR performs favorably against state-of-the-art nonblind and blind SR models. Code will be available at
https://github.com/XY-boy/Blind-Satellite-VSR
.