Results 51 to 60 of about 25,441 (258)
Lightweight Generative Network for Image Inpainting Using Feature Contrast Enhancement
IEEE Access, 2022 With the help of the convolutional neural network (CNN) and generative adversarial network (GAN), image inpainting has achieved remarkable advances in performance.
Qihui Han, Jie Liu, Cheolkon Jung
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Learning a Dilated Residual Network for SAR Image Despeckling
, 2018 In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a ...
Li, Jie +4 more
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Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions
IEEE Access, 2019 There has been an increasing interest in reducing the computational cost to develop efficient deep convolutional neural networks (DCNN) for real-time semantic segmentation.
Jaeseon Kim, Yong Seok Heo
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Enhanced CNN for image denoising
, 2019 Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train.
Fei, Lunke +5 more
core +1 more source
Efficient Keyword Spotting Using Dilated Convolutions and Gating [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in
Coucke, Alice +5 more
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A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
Applied Sciences, 2019 Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs.
Zilong Zhuang +4 more
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An effective modular approach for crowd counting in an image using convolutional neural networks
Scientific Reports, 2022 Abrupt and continuous nature of scale variation in a crowded scene is a challenging task to enhance crowd counting accuracy in an image. Existing crowd counting techniques generally used multi-column or single-column dilated convolution to tackle scale ...
Naveed Ilyas +3 more
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Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
, 2017 We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD).
D Schmauss +8 more
core +1 more source
DDCNet: Deep dilated convolutional neural network for dense prediction
Neurocomputing, 2023 Dense pixel matching problems such as optical flow and disparity estimation are among the most challenging tasks in computer vision. Recently, several deep learning methods designed for these problems have been successful. A sufficiently larger effective receptive field (ERF) and a higher resolution of spatial features within a network are essential ...
Salehi, Ali +1 more
openaire +3 more sources