  To achieve promising results on removing noise from real-world images, most
of existing denoising networks are formulated with complex network structure,
making them impractical for deployment. Some attempts focused on reducing the
number of filters and feature channels but suffered from large performance
loss, and a more practical and lightweight denoising network with fast
inference speed is of high demand.
  To this end, a \textbf{Thu}mb\textbf{n}ail based \textbf{D}\textbf{e}noising
Netwo\textbf{r}k dubbed Thunder, is proposed and implemented as a lightweight
structure for fast restoration without comprising the denoising capabilities.
Specifically, the Thunder model contains two newly-established modules:
  (1) a wavelet-based Thumbnail Subspace Encoder (TSE) which can leverage
sub-bands correlation to provide an approximate thumbnail based on the
low-frequent feature; (2) a Subspace Projection based Refine Module (SPR) which
can restore the details for thumbnail progressively based on the subspace
projection approach.
  Extensive experiments have been carried out on two real-world denoising
benchmarks, demonstrating that the proposed Thunder outperforms the existing
lightweight models and achieves competitive performance on PSNR and SSIM when
compared with the complex designs.
