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Akademický článek

Resource distribution of online and offline interactive teaching mode based on feature fusion network

Autor: Wu Wencong

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

This paper combines the text similarity calculation of co-occurring words, text similarity calculation of semantic similarity, text similarity calculation of cosine similarity, and text similarity calculation based on a language network and joins the

Externí odkaz: https://doaj.org/article/0164e4141aac4ef9bb2fb344cde03487

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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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Dual Residual Attention Network for Image Denoising

Autor: Wu, Wencong, Liu, Shijie, Zhou, Yi, Zhang, Yungang, Xiang, Yu

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) genera

Externí odkaz: http://arxiv.org/abs/2305.04269

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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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