Pyramidal convolution attention generative adversarial network with data augmentation for image denoising
| Autor: | Xiaojing Yang, Yaling Liu, Rui Li, Dongliang Xia, Qiongshuai Lyu |
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| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
0209 industrial biotechnology Computer science Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computational intelligence Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Residual Theoretical Computer Science Convolution 020901 industrial engineering & automation Kernel (image processing) Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Geometry and Topology Algorithm Software Block (data storage) Generator (mathematics) |
| Zdroj: | Soft Computing. 25:9273-9284 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-021-05870-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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