In order to make the image denoising more effective in high noise level environment, a gray image denoising method based on symmetric dilation residual network is proposed for additive Gaussian white noise. First, the incremental & dilated convolution, BN (Batch Normalization) and leaky ReLU (Rectified Linear Unit) are used to extract and learn features from the input noise image. Then the image is reconstructed by combining the descending & dilated convolution and ReLU. Finally, the effective separation between image and noise is realized by integrating deep residual network and BN. Experimental data of specific denoising model and stochastic denoising model show that this method achieves better objective results based on small resolution input & large resolution input and small dataset & large dataset compared with other methods. A series of subjective visual comparison results also show that proposed method can perform image denoising well in high noise level environment, and there are no problems such as damaged edge or texture details, boundary artifacts and poor definition of the denoised image. The proposed method not only improves the image denoising ability, but also improves the denoising productivity to some extent. In the related work, we have made a comprehensive discussion on the current mainstream image denoising methods based on traditional machine learning and deep learning, which to some extent makes up for the lack of such reviews. In addition, the existing problems and future development direction of image denoising are discussed in detail.
Multivariate time series long-term forecasting has always been the subject of research in various fields such as economics, finance, and traffic. In recent years, attention-based recurrent neural networks (RNNs) have received attention due to their ability of reducing error accumulation. However, the existing attention-based RNNs fail to eliminate the negative influence of irrelevant factors on prediction, and ignore the conflict between exogenous factors and target factor. To tackle these problems, we propose a novel Hierarchical Attention Network (HANet) for multivariate time series long-term forecasting. At first, HANet designs a factor-aware attention network (FAN) and uses it as the first component of the encoder. FAN weakens the negative impact of irrelevant exogenous factors on predictions by assigning small weights to them. Then HANet proposes a multi-modal fusion network (MFN) as the second component of the encoder. MFN employs a specially designed multi-modal fusion gate to adaptively select how much information about the expression of current time come from target and exogenous factors. Experiments on two real-world datasets reveal that HANet not only outperforms state-of-the-art methods, but also provides interpretability for prediction.
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