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Adversarial Partial Discharge Signal Reconstruction and Denoising With an encoder-decoder network

IEEE ACCESS 2024年 12卷 132271-132278页

作者： Firuzi, Keyvan Middle East Tech Univ METU Dept Elect & Elect Engn TR-06800 Ankara Turkiye

As the application of online partial discharge (PD) measurement increases the importance of denoising becomes increasingly obvious. Besides denoising a PD signal to detect and calculate discharge amplitude, pulse reconstructing is required to compute rise time, fall time and other features which play a crucial role in determining discharge severity and identifying defect types. In this paper, a deep learning method based on adversarial deep network by using encoder-decoder network as a generator is developed to perform a denoising and signal reconstruction task. The issue is to provide a database that contains both noisy and denoised data because every denoising method has its limitation, and it is not possible to train a network with real data and their perfect denoised pulses. Therefore, a synthetic database is developed, and signal deformation and noise added synthetically. The trained network's performance is evaluated under actual conditions in two distinct laboratories on various days, with differing noise levels and waveforms. The proposed method outperforms the wavelet method in denoising synthetic data and shows an improvement on real data, while successfully reconstructing the PD pulses. Enhancing the network's performance further, it underwent fine-tuning with actual noise, which led to a marked enhancement in its denoising ability and overall capabilities.

关键词： Partial discharges denoising signal reconstruction signal reconstruction wavelet denoising wavelet denoising encoder-decoder network encoder-decoder network adversarial network adversarial network encoder-decoder network adversarial network

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Single image deraining via deep residual attention and encoder-decoder network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3453-3467页

作者： Wei, Mingrun Wang, Hongjuan Cheng, Ru Yu, Yue Wang, Lukun Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An Shandong Peoples R China

Single image rain removal remains a crucial and challenging low-level image processing task while significantly for outdoor-based high-level computer vision tasks. Recently, deep convolutional neural networks (CNNs) have become the mainstream structure of removing rain streaks and obtained remarkable performance. However, most of the existing CNN-based methods mainly pay attention to completely removing rain streaks while neglecting the restoration of details after deraining, which suffer from poor visual performance. In this paper, we propose a deep residual attention and encoder-decoder network to overcome the above shortcoming. Specifically, we develop an excellent basic block that contains dual parallel paths which are called rain removal network and detail restore network, respectively, to perform entirely and in-depth mapping relationships from rain to no-rain. The upper rain removal network is composed of dilated convolution and channel attention. This combination can explore the correlation between features from the dimensions of spatial and channel. Meanwhile, for the lower detail restore network, we construct a simple yet effective symmetrical encoder-decoder structure to prevent the loss of global structures information and encourage the details back. Furthermore, our network is end-to-end trainable, easy to implement and without giant parameter quantity. Extensive experiments on synthetic and real-world datasets have shown that our DRAEN achieves better accuracy and visual improvements against recent state-of-the-art methods.

关键词： Single image deraining encoder-decoder network image processing feature fusion

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LF-SegNet: A Fully Convolutional encoder-decoder network for Segmenting Lung Fields from Chest Radiographs

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期101卷 511-529页

作者： Mittal, Ajay Hooda, Rahul Sofat, Sanjeev Panjab Univ Univ Inst Engn & Technol Dept Comp Sci & Engn Chandigarh India Punjab Engn Coll Deemed Be Univ Dept Comp Sci & Engn Chandigarh India

Segmentation of lung fields is an important pre-requisite step in chest radiographic computer-aided diagnosis systems as it precisely defines the region-of-interest on which different operations are applied. However, it is immensely challenging due to extreme variations in shape and size of lungs. Manual segmentation is also prone to large inter-observer and intra-observer variations. Thus, an automated method for lung field segmentation with sufficiently high accuracy is unsparingly required. This paper presents a deep learning-based fully convolutional encoder-decoder network for segmenting lung fields from chest radiographs. The major contribution of this work is in the unique design of the encoder-decoder network that makes it especially suitable for lung field segmentation. The proposed network is trained, tested and evaluated on publicly available standard datasets. The result of evaluation indicates that the performance of the proposed method, i.e. accuracy of 98.73% and overlap of 95.10%, is better than state-of-the-art methods.

关键词： Medical image analysis Deep-learning Lung field segmentation Chest radiographs encoder-decoder network

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A Detection and Verification Model Based on SSD and encoder-decoder network for Scene Text Detection

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IEEE ACCESS 2019年 7卷 71299-71310页

作者： Gao, Xue Han, Siyi Luo, Cong South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Guangdong Peoples R China SCUT Zhuhai Inst Modern Ind Innovat Zhuhai 519000 Peoples R China

Text detection in natural scene image is challenging due to text variation in size, orientation, color and complex background, contrast, and resolution. In this paper, we focus on the long text detection in complex background. In order to deal with multi-scale text variation and exploit the recognition result to enhance the detection performance, we propose a detection and verification model based on SSD and encoder-decoder network for scene text detection. First, we present a text localization neural network based on SSD, which incorporates a text detection layer into the standard SSD model and can detect horizontal texts, especially long and dense Chinese texts in natural scenes more effectively. Second, a text verification model based on the encoder-decoder network is designed to recognize and verify the initial detection results, in order to eliminate non-text areas that are falsely detected as text areas. A series of experiments have been conducted on our constructed horizontal text detection dataset, which is composed of the horizontal text images in ICDAR 2017 Competition on Reading Chinese Text in the Wild (RCTW 2017) and some scene images taken by cameras. Compared with previous approaches, experimental results show that our method has achieved the highest recall rate of 0.784 and competitive precision rate in text detection, indicating the effectiveness of our proposed method.

关键词： Scene text detection SSD encoder-decoder network verification model

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IMG2HEIGHT: height estimation from single remote sensing image using a deep convolutional encoder-decoder network

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第18期44卷 5686-5712页

作者： Du, Shouhang Xing, Jianghe Du, Shihong Cui, Ximin Xiao, Xiongwu Li, Wei Wang, Shaoyu China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China Shanghai Surveying & Mapping Inst Key Lab Spatial Temporal Big Data Anal & Applicat Shanghai Peoples R China Peking Univ Inst Remote Sensing Beijing Peoples R China Peking Univ GIS Beijing Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China

Height estimation from single remote sensing image is a challenging inherently ambiguous and technically ill-posed problem that we address in this study by resorting to deep learning approach. A spatial enhanced and multi-scale aggregated encoder-decoder network, SM-EDNet, is proposed, which takes a single image as input and produces an estimated height map as output. First, residual network (ResNet) is applied to extract low-level and deep features to cope with the heterogeneous characteristics of remote sensing scenes. Then, the multi-scale context information is aggregated through DenseASPP (Dense Atrous Spatial Pyramid Pooling) by extracting features from multiple dilated convolution layers. The skip connection is constructed by using the structure preserving model, DULR, to aggregate ResNet low-level features and multi-scale high-level features. The deformable convolution module is constructed to enhance the sensitivity to differences in geometric shapes of ground objects. For model training, three-layer deep supervision mechanism is designed to counteract the adverse effects of unstable gradients changes. Experimental results on three benchmark datasets, including ISPRS Vaihingen, ISPRS Potsdam, and DFC2018, show that the proposed method achieves the most outstanding performance compared with the state-of-the-art networks. The source codes are available at: https://***/xjh0929/2HEIGHT.

关键词： Single remote sensing image height estimation encoder-decoder network DenseASPP deep supervision mechanism ResNet

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REDN: A Recursive encoder-decoder network for Edge Detection

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IEEE ACCESS 2020年 8卷 90153-90164页

作者： Le, Truc Duan, Ye Univ Missouri Dept EECS Columbia MO 63105 USA

In this paper, we introduce REDN: A Recursive encoder-decoder network with Skip-Connections for edge detection in natural images. The proposed network is a novel integration of a Recursive Neural network with an encoder-decoder architecture. The recursive network enables iterative refinement of the edges using a single network model. Adding skip-connections between encoder and decoder helps the gradients reach all the layers of a network more easily and allows information related to finer details in the early stage of the encoder to be fully utilized in the decoder. Based on our extensive experiments on popular boundary detection datasets including BSDS500 [1], NYUD [2] and Pascal Context [3], REDN significantly advances the state-of-the-art on edge detection regarding standard evaluation metrics such as Optimal Dataset Scale (ODS) F-measure, Optimal Image Scale (OIS) F-measure, and Average Precision (AP).

关键词： Image edge detection Convolution Decoding Machine learning Task analysis Fuses Detectors Edge detection deep learning encoder-decoder network recursive network

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Light encoder-decoder network for road extraction of remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2019年第3期13卷

作者： He, Hao Yang, Dongfang Wang, Shicheng Zheng, Yuhang Wang, Shuyang Xian Hitech Res Inst Dept Control Engn Xian Shaanxi Peoples R China X1an Hitech Res Inst Dept Informat Engn Xian Shaanxi Peoples R China

Extracting roads from remote sensing images is an important task in the remote sensing field. We propose an approach of designing a light encoder-decoder network for road extraction. We analyze the relationship between road features and the receptive field of encoder-decoder networks and point out that the light encoder-decoder network can be achieved by controlling its receptive field. Based on this, we design road extraction networks on the architecture of a general encoder-decoder network, according to data specifications. In addition, we propose an adaptive weighted binary cross-entropy loss function to solve the problem of data imbalance in the training process. We validate our approach on the Massachusetts roads dataset and the DeepGlobe road extraction dataset. The experimental results show that our method reduces 98% and 94% of the parameters, respectively, compared with general encoder-decoder networks, whereas the performance of road extraction keeps well. Our approach has fewer parameters and good performance, so it is easier to deploy on mobile platforms. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote sensing road extraction deep learning encoder-decoder network receptive field

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Brain MRI Super-Resolution Using 3D Dilated Convolutional encoder-decoder network

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IEEE ACCESS 2020年 8卷 18938-18950页

作者： Du, Jinglong Wang, Lulu Liu, Yulu Zhou, Zexun He, Zhongshi Jia, Yuanyuan Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Three Gorges Univ Coll Comp Sci & Engn Chongqing 404130 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

The spatial resolution of magnetic resonance images (MRI) is limited by the hardware capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep convolutional neural networks (CNN) have achieved impressive success in MRI super-resolution (SR) reconstruction. Increasing network depth or width can enlarge the receptive field to improve SR accuracy, however, it is impractical for MRI reconstruction in clinical applications because of high computational loads. To address this issue, we propose a novel dilated convolutional encoder-decoder (DCED) network to improve the resolution of MRI. We exploit three-dimensional (3D) dilated convolutions as encoders to extract high-frequency features. The dilated encoders capture wider contextual information by exponentially enlarging the receptive field, without introducing additional parameters or layers. Then we decode the features using deconvolution operations to alleviate gridding artifacts and restore fine details. To improve information flow, the encoders and decoders are aggregated into symmetrically connected blocks. The output of each block is passed to the final convolution layer, which facilitates to extract hierarchical features. In addition, we also exploit a geometric self-ensemble 3D wavelet fusion method to improve the potential performance of MRI SR. Experimental results on four public available brain datasets show that our proposed method outperforms NLM (non-local means), LRTV (low-rank and total variation) and current CNN-based SR methods, which demonstrates that our method achieves a new state-of-the-art performance in MRI SR task.

关键词： Magnetic resonance image super-resolution reconstruction dilated convolutional neural networks encoder-decoder network

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CED-Net: contextual encoder-decoder network for 3D face reconstruction

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MULTIMEDIA SYSTEMS 2022年第5期28卷 1713-1722页

作者： Zhu, Lei Wang, Shanmin Zhao, Zengqun Xu, Xiang Liu, Qingshan Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing 210044 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

This paper proposes a contextual encoder-decoder network to regress UV position map for 3D face reconstruction, named CED-Net. Specifically, the CED-Net introduces contextual information both in the shape and feature level, which is served as the crucial information. Firstly, shape context relationship is considered as the loss function constraint, in which the intrinsic Euclidean norm and vector angle similarity are computed for each contextual vector. Furthermore, to add the contextual information at the feature level, the local feature correlation modulator is incorporated into the network's center section, which directs the network to capture the relationship between facial features from a spatial perspective. The heatmaps of the learned features demonstrate that the proposed CED-Net can pay attention to the comprehensive facial regions for reconstruction. The quantitative and qualitative experiments conducted on AFLW2000-3D and AFLW-LPFA show that the proposed method achieves superior results in both benchmarks.

关键词： 3D face reconstruction encoder-decoder network Shape context Local correlation

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Building Extraction of Aerial Images by a Global and Multi-Scale encoder-decoder network

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REMOTE SENSING 2020年第15期12卷 2350-2350页

作者： Ma, Jingjing Wu, Linlin Tang, Xu Liu, Fang Zhang, Xiangrong Jiao, Licheng Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Int Res Ctr Intelligent Percept & Computat Minist EducSch Artificial IntelligenceKey Lab I Xian 710071 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Semantic segmentation is an important and challenging task in the aerial image community since it can extract the target level information for understanding the aerial image. As a practical application of aerial image semantic segmentation, building extraction always attracts researchers' attention as the building is the specific land cover in the aerial images. There are two key points for building extraction from aerial images. One is learning the global and local features to fully describe the buildings with diverse shapes. The other one is mining the multi-scale information to discover the buildings with different resolutions. Taking these two key points into account, we propose a new method named global multi-scale encoder-decoder network (GMEDN) in this paper. Based on the encoder-decoder framework, GMEDN is developed with a local and global encoder and a distilling decoder. The local and global encoder aims at learning the representative features from the aerial images for describing the buildings, while the distilling decoder focuses on exploring the multi-scale information for the final segmentation masks. Combining them together, the building extraction is accomplished in an end-to-end manner. The effectiveness of our method is validated by the experiments counted on two public aerial image datasets. Compared with some existing methods, our model can achieve better performance.

关键词： building extraction aerial image encoder-decoder network

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