一种注意力机制引导的双卷积神经网络图像去噪算法

A Dual Convolutional Neural Network Image Denoising Algorithm Guided by Attention Mechanism

摘要: 近年来深度卷积神经网络在图像去噪中的应用引起了越来越多的研究兴趣。然而，对于复杂的任务，如真实的噪声图像，普通网络无法恢复精细的细节。提出了一种经过注意力机制引导的双重去噪网络来恢复干净的图像。具体来说，该网络由四个模块组成，扩张特征提取块Dilated Feature Extraction Block (DFEB)、动态卷积块Dynamic convolution structure diagram、注意力模块，重建模块。具有稀疏机制的特征提取模块经由两个子网络提取全局和局部特征。增强块收集并融合全局和局部特征，为后者的网络提供补充信息。压缩块细化所提取的信息并压缩网络。最后，利用重建区块重建去噪影像。该网络具有以下优点：1) 双网络结构具有稀疏机制，可以提取不同的特征，增强去噪器的泛化能力。2) 融合全局和局部特征可以提取显著特征，从而恢复复杂噪声图像的细节。大量的实验结果表明，该网络有较好的去噪效果。

Abstract: In recent years, the application of deep convolutional neural networks in image denoising has attracted more and more research interest. However, for complex tasks, such as real noisy images, ordinary networks cannot recover fine details. A dual denoising network guided by attention mechanism is proposed to restore clean images. Specifically, the network consists of four modules: Dilated Feature Extraction Block (DFEB), Dynamic convolution structure diagram, attention module and reconstruction module. Feature extraction blocks with sparse mechanism extract global and local features through two subnetworks. Enhancement blocks collect and fuse global and local features to provide supplementary information to the latter’s network. The compressed block refines the extracted information and compresses the network. Finally, the reconstructed block is used to reconstruct the denoised image. The network has the following advantages: 1) the dual network structure has a sparse mechanism, which can extract different features and enhance the generalization ability of the noise reducer. 2) Fusion of global and local features can extract significant features to recover the details of complex noise images. A large number of experimental results show that the network has a good noise reduction effect.