In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version called EDDSR. There are three major innovations: first, we re-designed dense modules to extract hierarchical features directly from LR images and propagate the...
In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural...
除此之外,DCNN需要大量的带有人工标注信息的训练数据,这会耗费很多人力物力,甚至在某些领域根本无法获得数据量很大的数据集。为了解决这些问题,此文[1]结合了扩张卷积 (dilated convolution) 和密集连接 (dense connection),提出了Mixed-Scale Dense Convolutional Network (MS-D网络),MS-D网络和传统的DCNN相比,提高了...
convolutional neural network (AD-CNN) specially designed for water body extraction from Sentinel-2 imagery. On the one hand, AD-CNN exploits dense connections to allow uncovering deeper features while simultaneously characterizing multiple data complexities. On the other hand, the proposed model also ...
High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction - monaen/LightFieldReconstruction
layer (convolutional kernel, 2 × 2 × 2; step size, 2 × 2 × 2) allowed to reduce the number of the network parameters, minimize overfitting, and reduce the model’s complexity. On the other hand, the transition layer solved the problem of changing the number of ...
通过深度卷积神经网络(Deep Convolutional Neural Networks, DCNNs)将图像转换为模型的参数。然而,以上所有的方法要么使用全连接层,要么在参数化的展开UV空间上使用二维卷积,从而得到具有许多参数的大型网络。在本文中,我们提出了第一个非线性3DMMs,通过使用直接网格(direct mesh)卷积学习联合纹理和形状自动编码器。我们...
Ye et al.8 proposed a self-supervised learning framework for a monocular depth estimation Convolutional Neural Network (CNN) model, which is an improved version of DeConvNet19. They used the disparity information obtained by a classical stereo algorithm from a stereo image pair as a self-...
[19]F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In Proc. Conf. Computer Vision and Pattern Recognition (CVPR), pages 5162–5170, 2015. 3 [20]R. Mur-Artal, J. M. M. Montiel, and J. D. Tards. Orb-slam: A versati...
WebCNN is a browser-based Convolutional Neural Network framework. This is a personal project in the earliest stages of development, which I'm sharing publicly for those with academic interest. I have a live demo for the MNIST classification here:http://www.denseinl2.com/webcnn/digitdemo.html...