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All-round improvement in DCT-based blind image watermarking with visual enhancement via denoising autoencoder

COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Hu, Hwai-Tsu Hsu, Ling-Yuan Lee, Tung-Tsun Natl Ilan Univ Dept Elect Engn Ilan Taiwan

Discrete cosine transform (DCT)-based watermarking has received great success in the copyright protection of digital images. This study exploited the orthonormal expansion properties of DCT to achieve superior performance in computational efficiency and watermarking efficacy. In particular, we reformulated the relative modulation to allow the modification of paired DCT coefficients as if operable in the spatial domain. To account for human visual characteristics, we also adjusted the embedding strength adaptively according to image entropy. The use of a range regulation mechanism enabled perfect watermark retrieval. The pursuit of an ideal balance between robustness and imperceptibility could be benefited from the grey wolf optimizer. Using the above-discussed techniques, the proposed watermarking scheme demonstrates superior robustness in terms of bit error rate and normalized correlation coefficient with the peak signal-to-noise ratio tuned at around 38 dB. Compared to the regular DCT approach, the computational acceleration is on the scale of N-2/(log2N)(2), where N signifies the side length of the image block under processing. Furthermore, for the type of character-style watermark, we explored the feasibility of using a denoising autoencoder (DAE) to enhance the retrieved watermark comprehensibility. The proposed DAE was found to be capable of contributing a 60% reduction of the bit error rate, thus making the recovered watermark visually more recognizable.

关键词： Blind image watermarking relative modulation DCT-based orthonormal expansion grey wolf optimizer denoising autoencoder

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Infrared Dim and Small Target Detection Based on denoising autoencoder Network

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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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IC-U-Net: A U-Net-based denoising autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

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NEUROIMAGE 2022年 263卷 119586-119586页

作者： Chuang, Chun-Hsiang Chang, Kong-Yi Huang, Chih-Sheng Jung, Tzyy-Ping Natl Tsing Hua Univ Coll Educ Res Ctr Educ & Mind Sci Hsinchu Taiwan Natl Tsing Hua Univ Inst Informat Syst & Applicat Coll Elect Engn & Comp Sci Hsinchu Taiwan Natl Tsing Hua Univ Dept Educ & Learning Technol Hsinchu Taiwan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung Taiwan Elan Microelect Corp Dept Artificial Intelligence Res & Dev Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Coll Artificial Intelligence & Green Energy Hsinchu Taiwan Natl Taipei Univ Technol Coll Elect Engn & Comp Sci Taipei Taiwan Univ Calif San Diego Inst Engn Med La Jolla CA USA Univ Calif San Diego Inst Neural Computat La Jolla CA USA

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end -to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://***/roseDwayane/AIEEG .

关键词： EEG Artifact Removal Signal Reconstruction U-Net Independent Component Analysis ICLabel denoising autoencoder Deep Learning

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Accurate classification of Listeria species by MALDI-TOF mass spectrometry incorporating denoising autoencoder and machine learning

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JOURNAL OF MICROBIOLOGICAL METHODS 2022年 192卷 106378-106378页

作者： Li, Yunhong Gan, Zeyu Zhou, Xi Chen, Zhiwei Xiamen Univ Sch Elect Sci & Engn Dept Elect Sci Fujian Prov Key Lab Plasma & Magnet Resonance Res Xiamen Peoples R China Xiamen Univ Coll Mat Res Ctr Biomed Engn Xiamen Dept BiomatHigher Educ Key Lab Biomed Engn Fujian Xiamen Peoples R China Xiamen Univ Sch Elect Sci & Engn Dept Elect Sci Xiamen 361005 Peoples R China

Listeria monocytogenes belongs to the category of facultative anaerobic bacteria, and is the pathogen of listeriosis, potentially lethal disease for humans. There are many similarities between L. monocytogenes and other nonp-athogenic Listeria species, which causes great difficulties for their correct identification. The level of L. monocytogenes contamination in food remains high according to statistics from the Food and Drug Administration. This situation leads to food recall and destruction, which has caused huge economic losses to the food industry. Therefore, the identification of Listeria species is very important for clinical treatment and food safety. This work aims to explore an efficient classification algorithm which could easily and reliably distinguish Listeria species. We attempted to classify Listeria species by incorporating denoising autoencoder (DAE) and machine learning algorithms in matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition, convolutional neural networks were used to map the high dimensional original mass spectrometry data to low dimensional core features. By analyzing MALDI-TOF MS data via incorporating DAE and support vector machine (SVM), the identification accuracy of Listeria species was 100%. The proposed classification algorithm is fast (range of seconds), easy to handle, and, more importantly, this method also allows for extending the identification scope of bacteria. The DAE model used in our research is an effective tool for the extraction of MALDI-TOF mass spectrometry features. Despite the fact that the MALDI-TOF MS dataset examined in our research had high dimensionality, the DAE + SVM algorithm was still able to exploit the hidden information embedded in the original MALDI-TOF mass spectra. The experimental results in our work demonstrated that MALDI-TOF mass spectrum combined with DAE + SVM could easily and reliably distinguish Listeria species.

关键词： Listeria species MALDI-TOF denoising autoencoder Machine learning Support vector machines Convolutional neural networks

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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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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 face attribute based recommendation system via integrating denoising autoencoder and hash coding

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 90卷 107020-107020页

作者： Liu, Fan Chen, Zhiyu Ding, Yuhua Yang, Sai Zhang, Tao Hohai Univ Nanjing 210098 Peoples R China Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China Nantong Univ Nantong 226019 Peoples R China Yangzhou Univ Yangzhou 225127 Jiangsu Peoples R China

Nowadays, with the rapid development of commerce, how to effectively improve the performance of an recommendation system has aroused great concern. However, traditional recommendation system requires users to log in their accounts, which brings poor user experience. This paper presents a novel recommendation system by using face recognition technologies to extract face attribute information as the input automatically. The system first obtains the user information of identity, gender, age, and then gets feedback by expression analysis. Based on the acquired face attributes, we propose to extract compact binary user features by integrating denoising autoencoder and hash coding, which can effectively improve the computing *** hash features from DAE-H-Face and DAE are further combined to enhance the representation ability. Finally, Hamming similarity-based collaborative filtering is used for recommendation. Experimental results on the MovieLens database show that the proposed recommendation method has better effectiveness and robustness. Moreover, the results also demonstrate its advantages to the cold start problem.

关键词： Recommendation denoising autoencoder Face attribute Hash coding

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Taxonomy-aware collaborative denoising autoencoder for personalized recommendation

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APPLIED INTELLIGENCE 2019年第6期49卷 2101-2118页

作者： Zhang, Chunhong Li, Tiantian Ren, Zhibin Hu, Zheng Ji, Yang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.

关键词： Recommender systems Taxonomy denoising autoencoder Collaborative filtering

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denoising of partial discharge signal using a hybrid framework of total variation denoising-autoencoder

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MEASUREMENT 2023年 223卷

作者： Chaudhuri, Sayanti Ghosh, Saibal Dey, Debangshu Munshi, Sugata Chatterjee, Biswendu Dalai, Sovan Jadavpur Univ Dept Elect Engn Kolkata India

Partial discharge (PD) measurement is an essential task for assessing the condition of electrical insulations. Different types of noises, such as random noise and discrete spectral interference creep into the captured PD signals during their measurement. As a result, there is a need for an efficient denoising technique to reduce the effects of these disturbances during the PD measurement. This paper proposes a denoising scheme through a hybrid approach, which uses a total variation denoising filter followed by a denoising autoencoder. The proposed filter is used here to denoise partial discharge (PD) signal corrupted with artificially induced white Gaussian noise. The efficacy of the proposed work is evaluated with the help of some denoising metrics such as signal-tonoise ratio (SNR), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE). The results amply indicate the notable performance of the proposed scheme, compared to the existing state-of-the-art PD denoising techniques.

关键词： Condition monitoring denoising autoencoder Partial discharge Spectrogram Total variation denoising filter

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Trust-Aware Collaborative Filtering with a denoising autoencoder

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NEURAL PROCESSING LETTERS 2019年第2期49卷 835-849页

作者： Wang, Meiqi Wu, Zhiyuan Sun, Xiaoxin Feng, Guozhong Zhang, Bangzuo Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.

关键词： Collaborative filtering denoising autoencoder Social networks Trust information

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