  In recent years, real image super-resolution (SR) has achieved promising
results due to the development of SR datasets and corresponding real SR
methods. In contrast, the field of real video SR is lagging behind, especially
for real raw videos. Considering the superiority of raw image SR over sRGB
image SR, we construct a real-world raw video SR (Real-RawVSR) dataset and
propose a corresponding SR method. We utilize two DSLR cameras and a
beam-splitter to simultaneously capture low-resolution (LR) and high-resolution
(HR) raw videos with 2x, 3x, and 4x magnifications. There are 450 video pairs
in our dataset, with scenes varying from indoor to outdoor, and motions
including camera and object movements. To our knowledge, this is the first
real-world raw VSR dataset. Since the raw video is characterized by the Bayer
pattern, we propose a two-branch network, which deals with both the packed RGGB
sequence and the original Bayer pattern sequence, and the two branches are
complementary to each other. After going through the proposed co-alignment,
interaction, fusion, and reconstruction modules, we generate the corresponding
HR sRGB sequence. Experimental results demonstrate that the proposed method
outperforms benchmark real and synthetic video SR methods with either raw or
sRGB inputs. Our code and dataset are available at
https://github.com/zmzhang1998/Real-RawVSR.
