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Two-stage Progressive Residual Dense Attention Network for Image Denoising

Autor: Wu, Wencong, Ge, An, Lv, Guannan, Xia, Yuelong, Zhang, Yungang, Xiong, Wen

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoising models equally utilize the hierarchical features of noisy images

Externí odkaz: http://arxiv.org/abs/2401.02831

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Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection

Autor: Wu, Wencong, Liao, Shicheng, Lv, Guannan, Liang, Peng, Zhang, Yungang

In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost

Externí odkaz: http://arxiv.org/abs/2304.01620

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DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Autor: Wu, Wencong, Lv, Guannan, Duan, Yingying, Liang, Peng, Zhang, Yungang, Xia, Yuelong

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian

Externí odkaz: http://arxiv.org/abs/2304.01498

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