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Infrared Dim and Small Target Detection Based on denoising autoencoder Network

MOBILE NETWORKS & APPLICATIONS 2020年第4期25卷 1469-1483页

作者： Shi, Manshu Wang, Huan Nanjing Univ Sci & Technol Nanjing Peoples R China

The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as "noise" in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets.

关键词： Small target detection Infrared images denoising autoencoder End-to-end deep model Image gradient

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V-DAFT: visual technique for texture image defect recognition with denoising autoencoder and fourier transform

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第10期18卷 7405-7418页

作者： Si, Jongwook Kim, Sungyoung Kumoh Natl Inst Technol Dept Computer AI Convergence Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea Kumoh Natl Inst Technol Dept Comp Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea

Texture is the surface qualities and visual attributes of an object, determined by the arrangement, size, shape, density, and proportion of its fundamental components. In the manufacturing industry, products typically have uniform textures, allowing for automated visual inspections of the product surface to recognize defects. During this process, texture defect recognition techniques can be employed. In this paper, we propose a method that combines a convolutional autoencoder architecture with Fourier transform analysis. We employ a normal reconstructed template as defined in this study. Despite its simple structure and rapid training and inference capabilities, it offers recognition performance comparable to state-of-the-art methods. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals, which is essential for effective defect recognition as texture defects often exhibit characteristic changes in specific frequency ranges. The experiment evaluates the recognition performance using the AUC metric, with the proposed method showing a score of 93.7%. To compare with existing approaches, we present experimental results from previous research, an ablation study of the proposed method, and results based on the high-pass filter used in the Fourier mask.

关键词： Texture image Defect recognition denoising autoencoder Fourier transform Image reconstruction

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uDAS: An Untied denoising autoencoder With Sparsity for Spectral Unmixing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第3期57卷 1698-1712页

作者： Qu, Ying Qi, Hairong Univ Tennessee Adv Imaging & Collaborat Informat Proc Grp Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

Linear spectral unmixing is the practice of decomposing the mixed pixel into a linear combination of the constituent endmembers and the estimated abundances. This paper focuses on unsupervised spectral unmixing where the endmembers are unknown a priori. Conventional approaches use either geometrical-or statistical-based approaches. In this paper, we address the challenges of spectral unmixing with unsupervised deep learning models, in specific, the autoencoder models, where the decoder serves as the endmembers and the hidden layer output serves as the abundances. In several recent attempts, part-based autoencoders have been designed to solve the unsupervised spectral unmixing problem. However, the performance has not been satisfactory. In this paper, we first discuss some important findings we make on issues with part-based autoencoders. By proof of counterexample, we show that all existing part-based autoencoder networks with nonnegative and tied encoder and decoder are inherently defective by making these inappropriate assumptions on the network structure. As a result, they are not suitable for solving the spectral unmixing problem. We propose a so-called untied denoising autoencoder with sparsity, in which the encoder and decoder of the network are independent, and only the decoder of the network is enforced to be nonnegative. Furthermore, we make two critical additions to the network design. First, since denoising is an essential step for spectral unmixing, we propose to incorporate the denoising capacity into the network optimization in the format of a denoising constraint rather than cascading another denoising preprocessor in order to avoid the introduction of additional reconstruction error. Second, to be more robust to the inaccurate estimation of a number of endmembers, we adopt an l(21)-norm on the encoder of the network to reduce the redundant endmembers while decreasing the reconstruction error simultaneously. The experimental results demonstrate that t

关键词： Deep learning denoising autoencoder hyperspectral image (HSI) spectral unmixing

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Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

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IET SIGNAL PROCESSING 2020年第6期14卷 396-405页

作者： Phadikar, Souvik Sinha, Nidul Ghosh, Rajdeep NIT Silchar Dept Elect Engn Silchar Assam India Gauhati Univ Dept Informat Technol Gauhati Assam India

This study proposes a novel combination of independent component analysis (ICA) in conjunction with support vector machine (SVM) and denoising autoencoder (DA), for the first time, for removal of eyeblink artefacts from the corrupted electroencephalography (EEG). At first the eyeblink corrupted EEG signals are decomposed into independent components (ICs) using ICA, the corrupted-ICs are then identified using SVM as a classifier. From the corrupted-ICs, the artefacted segment is identified with a second SVM classifier and corrected by the pre-trained DA. Finally, inverse-ICA operation is applied on the remaining ICs and the corrected ICs to obtain the artefact-free EEG signal. The proposed methodology modifies only the portion corrupted with artefacts, and does not alter the uncorrupted part, thereby preserving the neural information in the original EEG. The proposed methodology was implemented to remove eyeblinks from the EEG data collected from the publicly available EEGLab data set. The results reveal that the proposed methodology is superior to the other recently reported methods in terms of the mutual information and average correlation coefficient. Further, the proposed method is automatic and does not require any intervention of the operator, whereas the other methods require intervention of the user.

关键词： electroencephalography medical signal processing support vector machines independent component analysis corrected ICs artefact-free independent component analysis support vector machines denoising autoencoder support vector machine eyeblink artefacts corrupted electroencephalography corrupted-ICs artefacted segment SVM classifier inverse-ICA operation remaining ICs

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A Joint Framework of denoising autoencoder and Generative Vocoder for Monaural Speech Enhancement

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 1493-1505页

作者： Du, Zhihao Zhang, Xueliang Han, Jiqing Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Inner Mongolia Univ Dept Comp Sci Hohhot 010021 Peoples R China

Conventional monaural speech enhancement methods usually enhance the magnitude spectrum of noisy speech and leave the phase unchanged. Recent studies suggest that phase is also important for both speech intelligibility and perceptual quality. Although deep learning exhibits great potential on enhancing the magnitude and phase spectra in complex spectrogram domain and waveform domain, complex spectrogram and waveform are always more difficult to predict than the magnitude spectrum due to lack of clear structure in them. In this study, a Mel-domain denoising autoencoder and a deep generative vocoder are stacked to form a joint framework for monaural speech enhancement, in which the clean speech waveform is reconstructed without using the phase. Specifically, a convolutional recurrent network (CRN) is employed as the denoising autoencoder to enhance the Mel power spectrum of noisy speech. Then, the enhanced Mel power spectrum is fed to a deep generative vocoder to synthesize the speech waveform. Furthermore, the denoising autoencoder and generative vocoder are jointly fine-tuned. Experimental results show that the proposed method significantly improves speech intelligibility and perceptual quality. More importantly, our method achieves much better generalization ability for untrained noises than previous methods.

关键词： Speech enhancement Vocoders Noise measurement Noise reduction Spectrogram Couplings Monaural speech enhancement joint framework denoising autoencoder generative vocoder

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A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium-ion batteries

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ENERGY SCIENCE & ENGINEERING 2024年第8期12卷 3390-3400页

作者： Xia, Wei Xu, Jinli Liu, Baolei Duan, Huiyun Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Hubei Peoples R China Jiujiang Vocat & Tech Coll Sch Mech & Elect Engn Jiujiang Jiangxi Peoples R China

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data-driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder-decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium-ion batteries. image

关键词： CNN-BiGRU denoising autoencoder lithium-ion batteries reconstruction loss remaining useful life

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Robust DoA Estimation Using denoising autoencoder and Deep Neural Networks

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IEEE ACCESS 2022年 10卷 52551-52564页

作者： Chen, Dawei Shi, Shuo Gu, Xuemai Shim, Byonghyo Harbin Inst Technol Sch Elect & Informat Engn Harbin 150006 Peoples R China Seoul Natl Univ Sch Elect & Comp Engn Seoul 08826 South Korea Seoul Natl Univ Inst New Media & Commun Seoul 08826 South Korea

As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean "repaired" input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.

关键词： Direction-of-arrival estimation Estimation Signal to noise ratio Feature extraction Noise reduction Covariance matrices Array signal processing DoA SNR denoising autoencoder deep neural networks mutual coupling

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Domain adaptation network based on hypergraph regularized denoising autoencoder

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第3期52卷 2061-2079页

作者： Wang, Xuesong Ma, Yuting Cheng, Yuhu China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

Domain adaptation learning aims to solve the classification problems of unlabeled target domain by using rich labeled samples in source domain, but there are three main problems: negative transfer, under adaptation and under fitting. Aiming at these problems, a domain adaptation network based on hypergraph regularized denoising autoencoder (DAHDA) is proposed in this paper. To better fit the data distribution, the network is built with denoising autoencoder which can extract more robust feature representation. In the last feature and classification layers, the marginal and conditional distribution matching terms between domains are obtained via maximum mean discrepancy measurement to solve the under adaptation problem. To avoid negative transfer, the hypergraph regularization term is introduced to explore the high-order relationships among data. The classification performance of the model can be improved by preserving the statistical property and geometric structure simultaneously. Experimental results of 16 cross-domain transfer tasks verify that DAHDA outperforms other state-of-the-art methods.

关键词： Domain adaptation Hypergraph denoising autoencoder Maximum mean discrepancy

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An end-to-end denoising autoencoder-based deep neural network approach for fault diagnosis of analog circuit

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第3期107卷 605-616页

作者： Yang, Yueyi Wang, Lide Chen, Huang Wang, Chong Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

Fault diagnosis of analog circuit is critical to improve safety and reliability in electrical systems and reduce losses. Traditional fault diagnosis methods of analog circuit usually rely on the hand design feature extractor and can not generalize well to other diagnosis domains. To address these issues, an end-to-end denoising autoencoder (EEDAE)-based fault diagnosis approach is proposed. The proposed approach includes denoising autoencoder (DAE) and a softmax classifier. The DAE is designed to automatically extract fault features from the raw time series signals without any signal processing techniques and diagnostic expertise, and then the softmax classifier is used to classify the fault mode of analog circuits. Specifically, we design a novel loss function by jointly minimizing reconstruction loss and classification loss to improve training efficiency. The proposed approach just has one training stage, in which the encoder, decoder, and classifier are trained simultaneously. The experimental results demonstrate that compared with traditional methods, the proposed method has higher accuracy and lower requirements on data.

关键词： Analog circuits Fault diagnosis denoising autoencoder End-to-end

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Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2015年第1期2015卷 1页

作者： Zhang, Zhaofeng Wang, Longbiao Kai, Atsuhiko Yamada, Takanori Li, Weifeng Iwahashi, Masahiro Nagaoka Univ Technol Nagaoka Niigata 9402188 Japan Shizuoka Univ Naka Ku Hamamatsu Shizuoka 4328561 Japan Tsinghua Univ Beijing 100084 Peoples R China

Deep neural network (DNN)-based approaches have been shown to be effective in many automatic speech recognition systems. However, few works have focused on DNNs for distant-talking speaker recognition. In this study, a bottleneck feature derived from a DNN and a cepstral domain denoising autoencoder (DAE)-based dereverberation are presented for distant-talking speaker identification, and a combination of these two approaches is proposed. For the DNN-based bottleneck feature, we noted that DNNs can transform the reverberant speech feature to a new feature space with greater discriminative classification ability for distant-talking speaker recognition. Conversely, cepstral domain DAE-based dereverberation tries to suppress the reverberation by mapping the cepstrum of reverberant speech to that of clean speech with the expectation of improving the performance of distant-talking speaker recognition. Since the DNN-based discriminant bottleneck feature and DAE-based dereverberation have a strong complementary nature, the combination of these two methods is expected to be very effective for distant-talking speaker identification. A speaker identification experiment was performed on a distant-talking speech set, with reverberant environments differing from the training environments. In suppressing late reverberation, our method outperformed some state-of-the-art dereverberation approaches such as the multichannel least mean squares (MCLMS). Compared with the MCLMS, we obtained a reduction in relative error rates of 21.4% for the bottleneck feature and 47.0% for the autoencoder feature. Moreover, the combination of likelihoods of the DNN-based bottleneck feature and DAE-based dereverberation further improved the performance.

关键词： Speaker recognition Bottleneck features denoising autoencoder Deep neural network Reverberant speech

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