因此,本文提出了一种基于类Unet结构的RGB-高光谱图像重建网络(Res2-Unet)。整个网络以Unet架构为基础,引入Res2Net[22]模块构建其骨干网络,利用Res2Net的残差连接、多尺度融合等特性可更加细粒度地提取图像的局部和全局特征,同时加入通道注意力机制[23-24]能自适应调节通道特征响应,编解码间的跳跃连接可充分融合不...
图像重建Res2Net通道注意力机制针对高光谱成像设备价格昂贵而难以推广应用的问题,利用深度学习网络从易获得的RGB图像重建高质量的高光谱图像.提出的Res2-Unet深度学习网络以Unet框架为基础,以Res2Net为主要模块构建其骨干网络,可以在更加细粒度级别提取局部和全局的图像特征.引入通道注意力机制自适应调节通道特征响应,并...
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Res2-Unet inherits the contracting-expansive structure of U-Net. It is featured by employing advanced network modules such as the residual and squeeze-and-excitation (SE) to enhance the segmentation capability. The residual module is utilized in both contracting and expansive paths for comprehensive...
On the DRIVE dataset, the Dice, IOU and AUC of our Res2Unet reach 0.8186, 0.6926 and 0.9772, respectively, which are better than that of Unet with 0.8109, 0.6817 and 0.9751. Importantly, the number of parameters of Res2Unet are about one-third of Unet. It means that Res2Unet has ...
Res2Net网络为了能够检测煤矿井下的煤量,预测和提高煤的利用率,同时节省电能,减少人力的监管和资源成本.利用煤矿安装的视频监控系统,采用非接触的方式通过Camshift算法对快速运动皮带上的煤量捕捉和跟踪,然后建立Res2-UNet模型来获得显著性信息,融合灰度,纹理,边缘等特征到单一的网络中,实现了皮带煤量的检测.模型利用...
Res2Net等多种语义分割模型,在此基础上设计了Res2-Unet多尺度路面裂缝分割网络模型.通过对常见通道注意力模块进行分析,将CBAM通道注意力模块引入Res2-Unet分割模型,构建了一种新的Res2Unet-CBAM网络模型用于路面裂缝图像的分割任务.将Res2Unet-CBAM模型与其他深度学习模型的实验结果进行对比,结果表明此模型具...
CA-Res2UNet++: a deep residual UNet-based method for brain tumor segmentation in multimodal MRIdoi:10.1117/12.2687868TumorsBrainImage segmentationMagnetic resonance imagingFeature extractionConvolutional neural networksIn recent years, segmentation of the multimodal brain tumor image puts forward high ...
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In this study, we introduce an innovative deep learning network, ATN-Res2Unet, designed to mitigate saturation artifacts in endoscopic OCT images. This is achieved through the integration of multi-scale perception, multi-attention mechanisms, and frequency domain filters. To address the challenge of...