  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, 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 paper
proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by
considering the pixel-wise blur levels in a coarse-to-fine manner.
Specifically, we employed multi-scale deformable 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, 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 module to adjust the solution space of sharp mid-feature,
resulting in flexible feature adaptation in multi-level domains. Quantitative
and qualitative evaluations on both simulated and real-world satellite videos
demonstrate that our BSVSR performs favorably against state-of-the-art
non-blind and blind SR models. Code will be available at
https://github.com/XY-boy/Blind-Satellite-VSR
