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pro vyhledávání: '"Zheng, Menghua"'
Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better denoising performance. Window transformer can use long- and sh
Externí odkaz:
http://arxiv.org/abs/2407.05709
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal te
Externí odkaz:
http://arxiv.org/abs/2403.05807
Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To
Externí odkaz:
http://arxiv.org/abs/2403.02211
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for c
Externí odkaz:
http://arxiv.org/abs/2310.10408
Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, whic
Externí odkaz:
http://arxiv.org/abs/2209.12394
Publikováno v:
In Information Fusion February 2024 102
Publikováno v:
In Pattern Recognition February 2023 134
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