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Pyramidal convolution attention generative adversarial network with data augmentation for image denoising
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DOI：10.1007/s00500-021-05870-7.png)
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Abstract
En 中文
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.
Keywords:
Generative adversarial network
Attention mechanism
Convolution
Data augmentation
Denoising
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