  Low-light raw denoising is an important and valuable task in computational
photography where learning-based methods trained with paired real data are
mainstream. However, the limited data volume and complicated noise distribution
have constituted a learnability bottleneck for paired real data, which limits
the denoising performance of learning-based methods. To address this issue, we
present a learnability enhancement strategy to reform paired real data
according to noise modeling. Our strategy consists of two efficient techniques:
shot noise augmentation (SNA) and dark shading correction (DSC). Through noise
model decoupling, SNA improves the precision of data mapping by increasing the
data volume and DSC reduces the complexity of data mapping by reducing the
noise complexity. Extensive results on the public datasets and real imaging
scenarios collectively demonstrate the state-of-the-art performance of our
method. Our code is available at: https://github.com/megvii-research/PMN.
