Hyperspectral image (HSI)multispectral image (MSI)residual selective kernel attention-based U-netimage fusionThe fusion of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI) is a crucial technology for producing high-resolution hyperspectral images. Most existing...
This paper presents CSAU-Net, a cross-scale attention-guided U-Net, which is a combined CNN-transformer structure that leverages the local detail depiction of CNNs and the ability of transformers to handle long-distance dependencies. To integrate global context data, we propose a cross-scale ...
TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method 来自 科研支点 喜欢 0 阅读量: 30 作者:C Li,Z Li,W Liu 摘要: Retinal vessel segmentation plays a vital role in the medical field, facilitating the identification of numerous chronic ...
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中缺乏科学决策的问题,从而提高图像融合效果。 创新点 提出了一个融合多尺度特征的双U-Net框架,结合了U-Net网络和嵌套连接网络,以保留更多的上下文...
Attention-based U-Net model can help to extract salient local-level features that can be passed to the decoder part of the network. Considering the above issues, we propose a variant of the U-Net model called the Attention-based Residual Light U-Net model. The proposed model is effective ...
CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation 2022, Computers in Biology and Medicine Citation Excerpt : In order to better identify thin vessels, Zhang et al. [40] aggregated multi-scale information by using the attention mechanism and obtained the multi-output thro...
Attention U-Net (Oktay et al., 2022) introduces the Attention Gate (AG), which uses deeper semantics to control shallow features in the skip connections. Other notable studies, such as (i) applying different attention weights to multiple branches (Qin et al., 2018) and (ii) incorporating ...
The paper proposes a novel FloodDetectionNet model for flood area segmentation. The proposed model incorporates attention gates for semantic segmentation by utilizing advanced image segmentation techniques using a modified U-Net architecture. The attention gates focus on critical features gathered from multi...
Attention U-Net: Learning Where to Look for the Pancreas 的实现对显著性区域的关注,以及对无关背景区域的抑制。注意力模型可以很好的嵌入到CNN框架中,而且不增加计算量的同时提高模型性能。 3.方法4. 结果对比 可视化注意力机制加入对于特征的影响在CT数据集上取得了最好的结果 5. 总结 个人理解:利用下采样层...
Notably, our approach achieved a Dice coefficient of 81.66% on the BUSI dataset for breast lesion segmentation while using 26.11 million fewer parameters than U-Net, offering an effective balance between complexity and performance that meets the stringent requirements of medical imaging applications. ...