Pyramidal convolution attention generative adversarial network with data augmentation for image denoising

Generative adversarial networks (GANs) have shown remarkable effects for various computer vision tasks. Standard convolution plays an important role in the GAN-based model. However, the single type of kernel with a single spatial size limits the learning ability of the model and does not explicitly consider the dependencies among channels. To overcome these issues, this paper proposes a pyramidal convolution attention GAN for image denoising, a model that uses a residual structure with a pyramidal convolution attention block (PyCA) instead of the stacked standard convolution as a generator within the GAN setting. The proposed PyCA considers the channel-wise dependencies while extracting multi-scale features. Besides, we also design a data augmentation method for image denoising. The experimental results show that our model achieves better denoising performance than other competing methods.