Aiming at the above problems, we propose a rain mark removal algorithm based on the combination of dual-attention mechanism U-Net and multi-convolution. First, we add a double attention mechanism to the encoder of U-Net. It can give different weights to the rain mark features that need to...
In this network, we use U-net as the baseline and embed a dual-branch attention module in it. One of the branches provides channel attention via emphasizing feature association among different channel maps, while the other branch provides spatial attention which adaptively aggregates the features ...
本文将详细记录实现“语义分割双重注意力网络(Dual Attention Network)”的过程,涵盖从参数解析到性能调优等关键步骤。 在过去的几年来,随着深度学习的发展,语义分割技术经历了几个阶段的发展。起初是依赖于传统卷积神经网络(CNN)的模型,如FCN和SegNet。随后,随着U-Net和DeepLab等模型的出现,分割精度大幅提升。双重注意...
The proposed model, DARU-Net, is based on U-Net, and it improves the segmentation results after introducing two attention mechanisms. It proves that introduction of spatial attention and channel attention mechanism is of great help in identifying uterine fibroids. In most images, the signal ...
attention的目的就是在decoder的时候每个输出能关注到输入重点的部分,而不是均匀的关注,也就是相当于乘上不同的加权系数,而这个系数的计算过程是将前一个隐层状态和encoder的每一个隐层节点杜比,通过一个函数来获得对齐的可能性,这个函数后加上一个softmax层就得到了每部分对齐的概率,最后加权求和得到总的v和u,代...
论文链接:Attention based dual UNET network for infrared and visible image fusion | Multimedia Tools and Applications (springer.com) 研究目的 提出一种基于注意力机制的双U-Net网络,用于红外和可见光图像的融合。这种方法旨在通过精确提取源图像的有效信息和轮廓特征,解决现有融合算法在特征信息提取和融合权重分配...
The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension...
thus mitigating the burden of manual image analysis. This study introducesFocusU2Net, an innovative bi-level nested U-structure integrated with a dual-attention mechanism. The model integrates Focus Gate (FG) modules for spatial and channel-wise attention and Residual U-blocks (RSU) with multi-sc...
An anatomical feature-driven dual-attention 3DU‐Net convolutional neural network (AF-DA3DU-Net) is proposed by involving the anatomical features. These features encompass the anatomical information of the planning target volumes (PTVs) and the organs-at-risk (OARs), along with the spatial relati...
Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced residual blocks for medical ... Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced ...