Results 21 to 30 of about 1,718,101 (355)
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Computer Vision and Pattern Recognition, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi +7 more
semanticscholar +1 more source
Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Journal of Computer Networks and Communications, 2022 Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
doaj +1 more source
DISTRIBUTED CONVOLUTIONAL NEURAL NETWORK MODEL ON RESOURCE-CONSTRAINED CLUSTER [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper presents the distributed deep learning particularly convolutional neural network problem for resource-constrained devices.
Rezeda R. Khaydarova +3 more
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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE +3 more
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Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
openaire +3 more sources
Artificial Neural Networks and Evolutionary Computation in Remote Sensing [PDF]
, 2021 Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images.
core +1 more source
Orthogonal Convolutional Neural Networks [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix ...
Jiayun Wang +3 more
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Voronoi Convolutional Neural Networks
CoRR, 2020 Technical ...
Soroosh Yazdani, Andrea Tagliasacchi
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Offline Handwritten Chinese Character Recognition Based on DBN and CNN Fusion Model
Journal of Harbin University of Science and Technology, 2020 Aiming at the problem that some offline handwritten Chinese characters are similar in shape and it is difficult to extract the feature of characters and the recognition is not accurate, a convolutional neural network and deep belief network fusion model ...
LI Lanying, ZHOU Zhigang, CHEN Deyun
doaj +1 more source
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
Fausto Milletarì +2 more
semanticscholar +1 more source