  Convolutional layers in Artificial Neural Networks (ANN) treat the channel
features equally without feature selection flexibility. While using ANNs for
image denoising in real-world applications with unknown noise distributions,
particularly structured noise with learnable patterns, modeling informative
features can substantially boost the performance. Channel attention methods in
real image denoising tasks exploit dependencies between the feature channels,
hence being a frequency component filtering mechanism. Existing channel
attention modules typically use global statics as descriptors to learn the
inter-channel correlations. This method deems inefficient at learning
representative coefficients for re-scaling the channels in frequency level.
This paper proposes a novel Sub-band Pyramid Attention (SPA) based on wavelet
sub-band pyramid to recalibrate the frequency components of the extracted
features in a more fine-grained fashion. We equip the SPA blocks on a network
designed for real image denoising. Experimental results show that the proposed
method achieves a remarkable improvement than the benchmark naive channel
attention block. Furthermore, our results show how the pyramid level affects
the performance of the SPA blocks and exhibits favorable generalization
capability for the SPA blocks.
