Multi-scale large receptive field feature distillation network for lightweight infrared image super-resolutionInfrared imaging plays a pivotal role in applications such as remote sensing, unmanned aerial vehicles, and security surveillance. However, current infrared detectors have low resolution, and ...
In the new framework, Inception-V4 are integrated with multi-scale receptive field to extract a multi-scale features to represent the hot rolled steel defects. A group of the AutoEncoders are trained to adaptively reduce the dimension of the multi-scale features to improve generalization ability ...
they adopted Deep Neural Networks (DNN) to extract rich features from images. They proposed a modification to the DNN by replacing the final convolutional layer and the max-pooling layer with modules having multiple receptive fields. This change facilitated the ...
This method introduces a multi-scale receptive field module to the Resnet101 backbone network, which alleviates the problem of global information loss caused by the deepening of the network layer. Meanwhile, the channel and spatial attention mechanism is introduced into the residual module, the ...
针对语义分割问题 semantic segmentation,这里使用 dilated convolutions 得到multi-scale context 信息来提升分割效果。 dilated convolutions: 首先来看看膨胀卷积 dilated convolutions , 图(a):就是一个常规的 3*3 卷积,1-dilated convolution 得到 F1, F1的每个位置的 receptive field 是 3×3 图(b): 在 F1...
能构建更深的网络,增大“receptive field” 模糊图像和清晰图像在数值上本身就比较相近,因此仅仅让网络学习两者的差异也够了 整体网络结构文中选择了K=3的“multi-scale architecture”,输入、输出的“Gaussian pyramid patches”大小为{256×256,128×128,64×64}。 B_{k},L_{k},S_{k} 分别表示模糊图像、重...
The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the...
GRFB-UNet: A new multi-scale attention network with group receptive field block for tactile paving segmentation - marenan/GRFB-Unet
Moreover, inspired by the transformer network, we design a novel multi-scale receptive field GAT to extract the local-global adjacent node-features and edges-features. Finally, a graph attention network and a softMax function are utilized for multi-receptive feature fusion and pixel-label ...
The literature [45] introduces the ResNet and SENet modules into the U-Net model to enhance the receptive field in the feature extraction stage. Show abstract Learning multi-level structural information for small organ segmentation 2022, Signal Processing Show abstract Semantic segmentation of ...