Convolutional neural networkDilated residual convolutionMulti-scale informationNeural Processing Letters - Due to the excellent performance of deep learning, more and more image denoising methods based on convolutional neural networks (CNN) are proposed, including dilated...doi:10.1007/s11063-022-10934-2Jia,...
Multi-scale Residual Block (MSRB) was designed. MSRB combined a multi-scale convolution module and residual connection to improve the feature extraction capability of the network. Multi-scale Attention Module (MSAM) was proposed, which could effectively strengthen useful features and suppress useless fe...
Multi-scale context aggregation: The basic context module has 7 layers that apply 3×3 convolutions with different dilation factors. The dilations are 1, 1, 2, 4, 8, 16, and 1。 这里主要通过不同的 different dilation factors 得到 multi-scale context。 注意这里的 context module 参数初始化是...
This also helps to efficiently increase the RF size of the convolution kernels. As can be seen, the RF size of the kernels in the convolution layers of the MB gradually increases as the training continues. The CMSFL module represents such an efficient approach to increasing RF size by applyin...
Feature maps with different receptive fields can be fused with the multiscale convolution layer. By establishing a short connection between the input and output feature maps, the residual module can effectively reduce the risk of gradient disappearance in the model’s training process while also ...
(MSCSA) module. Both of these modules are developed under the framework of self-attention learning from the multi-scale feature maps. Acting as the neck network, the MSCA rectifies the multi-scale features without changing the formats of its inputs and outputs. This renders a different way ...
In summary, a lightweight and efficient SR model, attention-based multi-scale residual network (AMSRN), is proposed in this paper. We use a residual ASPP to fully extract the multi-scale information; it adopts dilated convolutions at different dilation rates to expand the receptive field while...
Finally, a deep hybrid CNN network was proposed, and a multi-scale residual attention (MSRA) module was designed to improve the performance of HSI classification under the condition of limited training data. The main technical contributions of this work are summarized as follows. (1) A GANs-...
Multi-scale fusion for RGB-D indoor semantic segmentationShiyi Jiang, Yang Xu, Danyang Li & Runze Fan Scientific Reports volume 12, Article number: 20305 (2022) Cite this article 3292 Accesses Metrics details Abstract In computer vision, convolution and pooling operations tend to lose high-...
unlike a simple 1D CNN. One part is the convolution value (CV-Unit), and the second is the gated value (GV-Unit). The GV-Unit reacts as a gate to control the CV-Unit output by passing through the sigmoid function, transforming the GV-Unit value from 0 to 1 as output. A value ...