| 基于空间特征融合的双路径图像去噪网络 |
| 投稿时间：2023-05-04 修订日期：2023-11-01 点此下载全文 |
| 引用本文：祖雅婷,李梦琪,张艺萌,王赫.基于空间特征融合的双路径图像去噪网络[J].红外,2024,45(7):29~34 |
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| 中文摘要:深度卷积神经网络(Convolutional Neural Network, CNN)在图像去噪领域受到广泛关注。然而，随着网络深度的增加，大多数深度CNN会出现性能饱和、学习能力下降等问题。提出了一种结合局部和全局特征的双路径去噪网络，将两个不同结构的网络组合后构成一个双路径模型，增加网络的宽度，从而获得更多不同的特征。通过长路径连接融合全局和局部特征，增强层间相关性。注意力机制利用当前阶段引导前一阶段的输入，获得更多的特征。实验结果表明，我们提出的网络模型在Set12和BSD68两个数据集中的峰值信噪比(Peak Signal-to-Noise Ratio, PSNR)分别达到了32.95 dB和31.74dB。同时，主观视觉效果(如图像的边缘等细节)恢复得更好、更清晰。 |
| 中文关键词:图像去噪 深度学习 注意力机制 卷积神经网络 双路径 |
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| Dual-Path Image Denoising Network Based on Spatial Feature Fusion |
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| Abstract:Deep convolutional neural networks (CNNs) have attracted much attention in the field of image denoising. However, with the increase in network depth, most deep CNNs have problems such as performance saturation and learning decline. In this paper, a dual-path denoising network combining local and global features is proposed. Two networks with different structures are combined to form a dual path model, and the width of the network is increased to obtain more different features. The global and local features are integrated through long path connections to enhance interlayer correlation. The attention mechanism uses the current stage to guide the input of the previous stage to obtain more features. The experimental results show that the PSNR values of the proposed network model reach 32.95 dB and 31.74 dB in Set12 and BSD68 datasets, respectively. At the same time, the subjective visual effects such as image edges and other details are recovered better and clearer. |
| keywords:image denoising deep learning attention mechanism convolutional neural network dual path |
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