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
this https URL

该研究提出了一种实用的盲自适应卫星视频超分辨率算法（BSVSR），通过粗到精的方式考虑像素级模糊程度，探索更多的锐度线索，并使用金字塔空间转换模块在多层次领域中调整锐度中间特征的解空间，从而在模拟和实际卫星视频上表现出色，优于最先进的非盲和盲自适应超分辨率模型。

卫星视频的深度盲超分辨率

Deep Blind Super-Resolution for Satellite Video

Optical-flow-based and kernel-based approaches have been widely explored for
temporal compensation in satellite video super-resolution (VSR). However, these
techniques involve high computational consumption and are prone to fail under
complex motions. In this paper, we proposed to exploit the well-defined
temporal difference for efficient and robust temporal compensation. To fully
utilize the temporal information within frames, we separately modeled the
short-term and long-term temporal discrepancy since they provide distinctive
complementary properties. Specifically, a short-term temporal difference module
is designed to extract local motion representations from residual maps between
adjacent frames, which provides more clues for accurate texture representation.
Meanwhile, the global dependency in the entire frame sequence is explored via
long-term difference learning. The differences between forward and backward
segments are incorporated and activated to modulate the temporal feature,
resulting in holistic global compensation. Besides, we further proposed a
difference compensation unit to enrich the interaction between the spatial
distribution of the target frame and compensated results, which helps maintain
spatial consistency while refining the features to avoid misalignment.
Extensive objective and subjective evaluation of five mainstream satellite
videos demonstrates that the proposed method performs favorably for satellite
VSR. Code will be available at https://github.com/XY-boy/TDMVSR

本文提出了一种在卫星视频超分辨率（VSR）中实现高效而强健的时间补偿的方法，利用亮度图像中定义良好的时间差异来分别建模短期和长期时间差异，同时设计了差异补偿单元来保持空间一致性和避免错位。通过 extensive 的客观和主观评估，证明了该方法在卫星 VSR 中表现出色。