Abstract
Convolutional layers treat the Channel features equally with no prioritization. When Convolutional Neural Networks (CNNs) are used for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the denoising performance. Channel attentions in real-world image denoising tasks exploit dependencies between the feature channels; therefore, they can be viewed as a frequency-domain filtering mechanism. Existing channel attention modules typically use global statics as descriptors to learn inter-channel correlations. These methods deem inefficient in learning representative coefficients for re-scaling the channels at frequency level. This paper proposes a novel Sub-band Pyramid Attention (SPA) model based on wavelet transform to recalibrate the extracted features’ frequency components in a more fine-grained fashion. Our method, in one sense, integrates the conventional frequency-domain filtering methods with deep learning architectures to achieve higher performance records. Experimental results show that ANNs equipped with the proposed attention module substantially improves upon the benchmark naive channel attention blocks. More specifically, we obtained a 3.97 dB gain compared to the best traditional algorithm, BM3D and a 1.87 dB to 0.18 dB gain over the DL-based methods in terms of denoising performance. Furthermore, our results show how the pyramid level affects the performance of the SPA blocks and exhibits favorable generalization capability for the SPA blocks.
