Search Results - Residual learning convolutional denoising autoencoder

Automatic modulation classification of radar signals utilizing X-net by Chen, Kuiyu, Zhang, Jingyi, Chen, Si, Zhang, Shuning, Zhao, Huichang

“… Owing to the X-shaped structure, the recognition network is named X-net. A residual learning convolutional denoising autoencoder (RLCDAE…”
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Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering by Wang, Yang, Xiao, Hong, Zhang, Zhihai, Guo, Xiaoxuan, Liu, Qiang

“…) deep residual convolutional…”
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Low-dose CT restoration via stacked sparse denoising autoencoders by Liu, Yan, Zhang, Yi

“…), dictionary learning, block-matching 3D (BM3D), convolutional denoising autoencoders (CDA) and U-Net based residual convolutional neural network (KAIST-Net…”
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A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions by Shang, Zhiwu, Tang, Lutai, Pan, Cailu, Cheng, Hongchuan

“… Zero-shot learning (ZSL) can identify samples unseen during the training phase and has now been applied to the field of fault diagnosis…”
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Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model by Mei, Shuang, Wang, Yudan, Wen, Guojun

“… This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels…”
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Improving brain MRI denoising using convolutional AutoEncoder and sparse representations by Velayudham, A, Madhan Kumar, K., Krishna Priya, MS

“… However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP…”
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Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder by Shi, Hongmei, Chen, Jingcheng, Si, Jin, Zheng, Changchang

“… diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE…”
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Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising by Paul, Arati, Kundu, Ahana, Chaki, Nabendu, Dutta, Dibyendu, Jha, C. S.

“…Denoising of hyperspectral images (HSIs) is an important preprocessing step to enhance the performance of its analysis and interpretation…”
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ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising by Liu, Han, Liao, Peixi, Chen, Hu, Zhang, Yi

“… Although various low-dose CT methods using deep learning techniques have produced impressive results, convolutional neural network based methods focus more on local information and hence are very…”
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Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning by Tolooshams, Bahareh, Dey, Sourav, Ba, Demba

“…), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks…”
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Traffic sign classification with deformable convolution based on denoising residual convolutional autoencoder region localization by Pan, Hao, Guo, Qianlu, Yuan, Decheng, Pan, Duotao, Li, Dong, Yu, Qianxin

“… autoencoder for important region localization and deformable convolution (DRCAE-DCN). The denoising residual convolutional autoencoder is used to extract feature…”
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One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis by Yu, Jianbo, Zhou, Xingkang

“… In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way…”
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Deep Residual U-Net Autoencoder with Weighted Overlapping Reconstruction for EMG Signal Denoising by Mehmood, Atif, Wiora, Jozef

“… This paper introduces an advanced deep learning framework for EMG denoising, centred on a U-Net-inspired convolutional autoencoder with integrated residual blocks and skip connections…”
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Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising by Bouzianis, Nikolaos, Stathopoulos, Ioannis, Valsamaki, Pipitsa, Rapti, Efthymia, Trikopani, Ekaterini, Apostolidou, Vasiliki, Kotini, Athanasia, Zissimopoulos, Athanasios, Adamopoulos, Adam, Karavasilis, Efstratios

“…Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE…”
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Clutter Suppression in GPR Imaging Using an Improved Robust Convolutional Autoencoder by Fan, Pengjie, Shen, Shaoxiang, Wang, Guangyu

“… We propose a clutter suppression algorithm based on an improved robust convolutional autoencoder (U‐RCAE…”
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Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise by Feng, Deshan, Wang, Xiangyu, Wang, Xun, Ding, Siyuan, Zhang, Hua

“…). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuation…”
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Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model by Zhang, Hongwei, Liu, Shuting, Tan, Quanlu, Lu, Shuai, Yao, Le, Ge, Zhiqiang

“…‐patterned fabric images. Second, a multi‐scale U‐shaped denoising convolutional autoencoder was modelled using defect…”
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Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Shaheen, Ahmed, Ye, Liang, Karunaratne, Chrishni, Seppänen, Tapio

“… This study proposes a novel Fully-Gated Denoising Autoencoder (FGDAE) to significantly reduce the effects of different artifacts on ECG signals…”
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An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces by Mei, Shuang, Yang, Hua, Yin, Zhouping

“… This approach is carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results…”
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Enhancing Border Learning for Better Image Denoising by Ge, Xin, Zhu, Yu, Qi, Liping, Hu, Yaoqi, Sun, Jinqiu, Zhang, Yanning

“…Deep neural networks for image denoising typically follow an encoder–decoder model, with convolutional (Conv…”
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