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Multi-scale dilated convolution of convolutional neural network for image denoising
Multimedia Tools and Applications, 2019Convolutional Neural Network has achieved great success in image denoising. The conventional methods usually sense those beyond scope contextual info at the expense of the receptive filed shrinking, which easily lead to multiple limitations. In this paper, we have proposed a concise and efficient convolutional neural network naming Multi-scale Dilated ...
Yanjie Wang +3 more
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HDConv: Heterogeneous Kernel-Based Dilated Convolutions
Neural Networks, 2023Dilated convolution has been widely used in various computer vision tasks due to its ability to expand the receptive field while maintaining the resolution of feature maps. However, the critical challenge is the gridding problem caused by the isomorphic structure of the dilated convolution, where the holes filled in the dilated convolution destroy the ...
Haigen Hu +4 more
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Crowd Counting with Dilated Inception Convolution
2021 7th International Conference on Computing and Artificial Intelligence, 2021Convolutional neural network (CNN) has been successfully applied to image-based crowd density estimation. However, large computational resources are required in previous CNN-based methods. Therefore, to overcome these drawbacks, this paper proposes a lightweight crowd density map estimation architecture with Dilated Inception Convolution Neural Network
Chen Hua, Kuang Xu, Tong Tong
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Dilated Convolutions in Retinal Blood Vessels Segmentation
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), 2019Segmentation of retinal blood vessels allows a quantitative analysis of vessels, hence it helps to diagnose several cardiovascular and ophthalmologic diseases. Manual segmentation is time-consuming, therefore, an automatic method is needed. Dilated convolutions have been recently adopted for semantic segmentation task and higher performances have been ...
Lopes, Ana P. +2 more
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Enhancing Piano Transcription by Dilated Convolution
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020When detecting music pitch with deep learning, the most challenging issue is how to aggregate the multi-scale frequency information scattered in the spectrogram of a music signal. Traditionally, this aggregation was achieved via strided pooling and fully connected operations, or domain-specific sparse convolutions.
Xian Wang, Lingqiao Liu, Qinfeng Shi
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Limits of Dilated Convolution Transforms
SIAM Journal on Mathematical Analysis, 1985Let k be a kernel so that the convolution transform \(f\to k*f\) maps \(L^ p(R^ n)\) into \(L^ p(R^ n)\). The author studies the behaviour in \(L^ p\) of \(k_ t*f\), \(t>0\), as t goes to 0 or \(\infty\); here \(k_ t\) is the dilated kernel defined by \(k_ t(x)=t^{-n}k(t^{-1}x).\) In particular, he gives conditions on k which imply that \(\lim k_ t*f(x)
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Face completion with Hybrid Dilated Convolution
Signal Processing: Image Communication, 2020Abstract Image completion is a challenging task which aims to fill the missing or masked regions in images with plausibly synthesized contents. In this paper, we focus on face image inpainting tasks, aiming at reconstructing missing or damaged regions of an incomplete face image given the context information.
Yuchun Fang +4 more
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Efficient Dilated-Winograd Convolutional Neural Networks
2019 IEEE International Conference on Image Processing (ICIP), 2019Dilated convolution is used to achieve wide receptive fields in computer vision algorithms such as image segmentation and denoising. Unlike the strided convolution, dilated convolution maintains the resolution of the output feature map same as the input feature map.
Minsik Kim +4 more
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Anticipating Maneuvers with Dilated Convolutions
Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018Anticipation is an essential ability for any system designed for human robot interaction. As human activities are complex, the robot/machine should be capable of processing long time-series observations to understand them. These observations are normally high dimensional, corrupted, noisy, have a high frequency, and have very long temporal ...
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Environmental sound classification with dilated convolutions
Applied Acoustics, 2019Abstract In sound information retrieval (SIR) area, environmental sound classification (ESC) emerges as a new issue, which aims at classifying environments by analysing the complex features extracted from the various sound data. As one of the most efficient feature extraction methods, convolution neural networks (CNN) has made its success in speech ...
Yan Chen +4 more
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