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Seismic random noise suppression using deep convolutional autoencoder neural network

JOURNAL OF APPLIED GEOPHYSICS 2020年 178卷 104071-104071页

作者： Song, Hui Gao, Yang Chen, Wei Xue, Ya-juan Zhang, Hua Zhang, Xiang Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Daxue Rd 111 Wuhan 430100 Peoples R China Hubei Cooperat Innovat Ctr Unconvent Oil & Gas Daxue Rd 111 Wuhan 430100 Peoples R China China Univ Petr Key Lab Geophys Explorat CNPC Beijing 102249 Peoples R China Chengdu Univ Informat Technol Sch Commun Engn Chengdu 610225 Peoples R China East China Univ Technol State Key Lab Nucl Resources & Environm Nanchang 330013 Jiangxi Peoples R China

Due to human or environmental factors, random noise will inevitably be introduced during seismic data acquisition. Contaminated seismic data seriously affect subsequent seismic data processing and imaging. In this paper, we propose a deep convolutional autoencoder neural network for denoising, which consists of encoding and decoding frameworks. The encoding framework consisting of convolution layers and pooling layers captures features of seismic data while eliminating noise. The decoding framework consisting of convolution layers and upsampling layers is capable of enlarging the feature map and recovering the small scale features of the seismic data. In order to represent the characteristics of seismic data optimally, we introduce L-1 regularization to constrain the weights. In order to learn multi-scale features of seismic data, we use the multiple filters with different sizes in both encoding and decoding frameworks. The proposed method is tested on synthetic and field cases. The experimental results show that the proposed denoising method can suppress random noise effectively. (C) 2020 Published by Elsevier B.V.

关键词： Seismic data Noise suppression Unsupervised learning deep convolutional autoencoder

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Aerial image dehazing using a deep convolutional autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第39-40期79卷 29493-29511页

作者： Fazlali, Hamidreza Shirani, Shahram McDonald, Mike Brown, Daly Kirubarajan, Thia McMaster Univ ECE Dept 1280 Main St West Hamilton ON Canada

Aerial images and videos are extensively used for object detection and target tracking. However, due to the presence of thin clouds, haze or smoke from buildings, the processing of aerial data can be challenging. Existing single-image dehazing methods that work on ground-to-ground images, do not perform well on aerial images. Moreover, current dehazing methods are not capable for real-time processing. In this paper, a new end-to-end aerial image dehazing method using a deep convolutional autoencoder is proposed. Using the convolutional autoencoder, the dehazing problem is divided into two parts, namely, encoder, which aims extract important features to dehaze hazy regions and decoder, which aims to reconstruct the dehazed image using the down-sampled image received from the encoder. In this proposed method, we also exploit the superpixels in two different scales to generate synthetic thin cloud data to train our network. Since this network is trained in an end-to-end manner, in the test phase, for each input hazy aerial image, the proposed algorithm outputs a dehazed version without requiring any other information such as transmission map or atmospheric light value. With the proposed method, hazy regions are dehazed and objects within hazy regions become more visible while the contrast of non-hazy regions is increased. Experimental results on synthetic and real hazy aerial images demonstrate the superiority of the proposed method compared to existing dehazing methods in terms of quality and speed.

关键词： Aerial images and videos Dehazing deep convolutional autoencoder Airborne surveillance Wide area motion imagery

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MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7204-7238页

作者： Zheng, Qinghe Zhao, Penghui Zhang, Deliang Wang, Hongjun Shandong Univ Sch Informat Sci & Engn 72 Binhai Rd Qingdao 266237 Shandong Peoples R China Shandong Univ Publ Innovat Expt Teaching Ctr Qingdao Peoples R China

Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization-based deep convolutional autoencoder (MR-DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy-stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back-propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross-layer manifold invariance. Finally, MR-DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state-of-the-art performance. To help understand the principle behind MR-DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.

关键词： deep convolutional autoencoder manifold consistency manifold regularization positive-unlabeled problem unauthorized broadcasting identification

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Vessel Trajectory Similarity Measure Based on deep convolutional autoencoder 5

Vessel Trajectory Similarity Measure Based on Deep Convoluti...

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5th IEEE International Conference on Big Data Analytics (ICBDA)

作者： Li, Shichen Liang, Maohan Liu, Ryan Wen Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

ISBN: (纸本)9781728141114

With the Widespread use of Internet of Thing (IoT) technology and the extensive application of wireless communication technologies, innovational changes have been made in all walks of life. In the field of navigation, Automatic Identification System (AIS) is widely equipped in vessels, which can obtain continuous position data from vessels and assemble them to vessel trajectories. While the massive vessel trajectory data obtained through AIS make great difficulty in maritime data analysis, however, they also make the vessel trajectory similarity measure become a hot topic in spatial database research. In recent years, there have been many studies about trajectory similarity measure of maritime, but these methods only consider the calculation of relative position between trajectory points, and these methods are inefficient with a low accuracy of trajectory similarity measure. In this study, we propose a novel approach called convolutional autoencoder (CAE), for measuring vessel trajectory similarity based on convolutional Neural Network (CNN) and autoencoder (AE). In this model, the vessel trajectory was gridded, and the grid-based convolutional autoencoder was proposed to extract the trajectory data as feature vectors to represent the original trajectories. Then the low-dimensional feature vectors were used for estimating the original trajectory similarity. In addition, an experiment was conducted to prove the effectiveness of our model. Compared with the Frechet distance and Dynamic Time Warping (DTW) distance with the CAE, the results prove that CAE is capable of more efficient trajectory similarity computation and search.

关键词： Trajectory Similarity measure deep convolutional autoencoder Machine learning Data mining

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A deep convolutional autoencoder Architecture for Automatic Image Colorization

A Deep Convolutional Autoencoder Architecture for Automatic ...

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IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

作者： Cevallos, Stefano Perez, Noel Riofrio, Daniel Benitez, Diego Moyano, Ricardo Flores Baldeon-Calisto, Maria Univ San Francisco Quito USFQ Colegio Ciencias & Ingn El Politecn Quito 170157 Ecuador Univ San Francisco Quito USFQ CATENA USFQ Ingn Ind Quito 170157 Ecuador

ISBN: (数字)9781665474702

ISBN: (纸本)9781665474702

The inherent complexity of image colorization has motivated computer scientists towards the development of algorithms capable of simplifying the image colorization process. Despite the numerous advancements yielded by these efforts, there are still some limitations regarding the resulting image quality and its similarity to the ground truth counterpart. This paper proposes and implements a deep convolutional autoencoder architecture that maximizes the image colorization performance on two different datasets, the Fruit-360 and Flickr-Faces-HQ. To this end, a modification of the VGG16 model and a custom deep CNN model were assembled to predict and portray colors on grayscale images. We obtained mean absolute and square error results under the 0.01% on both datasets, demonstrating the substantial similarity between the output image and its ground truth counterpart. Additionally, the peak signal-to-noise ratio results of 27.72 (Fruits-360) and 26.86 (Flickr-Faces-HQ) indicate that the image colorization process introduces a relatively low drop in image quality.

关键词： image colorization deep convolutional autoencoder CNN VGG

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Adaptive Surrogate Estimation with Spatial Features Using a deep convolutional autoencoder for CO₂ Geological Sequestration

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ENERGIES 2021年第2期14卷 413页

作者： Jo, Suryeom Park, Changhyup Ryu, Dong-Woo Ahn, Seongin Korea Inst Geosci & Mineral Resources Geo ICT Convergence Res Team Daejeon 34132 South Korea Kangwon Natl Univ Dept Energy & Resources Engn Chunchon 24341 South Korea

This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not only reduce computational costs but also to extract dimensionality-reduced features to conserve spatial characteristics. The workflow integrates two different spatial properties within a single convolutional system, and it also achieves accurate reconstruction performance. This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920. The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of fully-connected layers. The DCAE reconstructs spatial parameters such as permeability and porosity while conserving their statistical values, i.e., their mean and standard deviation, achieving R-squared values of over 0.972 with a mean absolute percentage error of their mean values of less than 1.79%. The adaptive surrogate model using the latent features extracted by DCAE, well operations, and modeling parameters is able to accurately estimate CO2 sequestration performances. The model shows R-squared values of over 0.892 for testing data not used in training and validation. The DCAE-based surrogate estimation exploits the reliable integration of various spatial data within the fully-convolutional network and allows us to evaluate flow behavior occurring in a subsurface domain.

关键词： deep convolutional autoencoder deep learning spatial parameter latent feature surrogate model data integration

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Improving geometric P-norm-based glioma segmentation through deep convolutional autoencoder encapsulation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartB期71卷 103232-103232页

作者： Takrouni, Wiem Douik, Ali Univ Sousse Noccs ISITCom Sousse 4011 Tunisia Univ Sousse NOCCS ENISO Sousse 4054 Tunisia

Gliomas are the most common types of brain tumors. Their shapes are irregular and their boundaries are ambiguous, producing a tumor that is hard to detect. To improve the segmentation performance, we propose a PNorm-Based Glioma Segmentation by encapsulating it within a deep convolutional autoencoder (DCA). In this paper, we first develop a new robust deep P-norm Generative Model (DPGM) that can extract and classify the feature by the encoder section and segment this latter by the decoder section. After that, we utilise a deep Discriminative Model (DDM) which applies the conditional random fields to refine the segmentation result. Effectiveness is gained by gradient descent training on the set of pre-P-norm filtering and thresolding convolutional operations using a novel geometric P-norm-based loss function. This latter will be optimized by the RANSAC layer, which is geared toward optimizing the strength of the correct model relative to the most convincing false model. We evaluate the accuracy and robustness of our method within MRI sequences (T1, T2, T2c & FLAIR) from the Multimodal Brain Tumor Image Segmentation Challenge (BRATS 2013 Leaderboard, BRATS 2013 Challenge, BRATS 2015 and BRATS 2017). The experiments demonstrate a high-quality segmentation results of our DPGM-DDM architecture and the average Dice is as high as 97.2%, 97.6%, and 98.4% respectively for complete, core, and enhancing tumor regions. Overall, our deep proposed architecture is effective in segmenting gliomas in multimodal images or fair images, and has the potential in routine examinations of gliomas in daily clinical practice.

关键词： Generative-discriminative model deep convolutional autoencoder Geometric P-norm model Glioma segmentation Expectation-maximization algorithm

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The Channel Prediction Combining deep convolutional autoencoder and CNN-BiLSTM

International Core Journal of Engineering

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International Core Journal of Engineering 2023年第5期9卷 5-14页

作者： Yurun Zhang Xueyan Hu Wei Liu

Aiming at the problems of noise interference, rapid change of channel quality and outdated channel parameters in wireless communication transmission, a channel prediction model combining deep convolutional autoencoder and CNN‐BiLSTM (convolutional Neural Network ‐ Bidirectional Long Short ‐ Term Memory) is proposed. First, a deep convolutional autoencoder network is constructed as a preprocessing of channel prediction. Secondly, a hybrid model integrating CNN and BiLSTM is constructed, the local features of time series signal along the positive direction of the time axis are extracted by 1D CNN (one‐dimensional convolution), and the BiLSTM extracts the bidirectional global feature, and then fuse the extracted features, which is conducive to the prediction. Finally, transfer learning is adopted for small dataset to fine tune a new model. By comparing the proposed method and its variants on the dataset, the results show that the model proposed in this paper have certain advantages.

关键词： Channel Prediction deep convolutional autoencoder CNN BiLSTM Transfer Learning

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Genetic deep convolutional autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

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SENSORS 2020年第14期20卷 3829.-3829页

作者： Sadrawi, Muammar Lin, Yin-Tsong Lin, Chien-Hung Mathunjwa, Bhekumuzi Fan, Shou-Zen Abbod, Maysam F. Shieh, Jiann-Shing Yuan Ze Univ Dept Mech Engn Taoyuan 32003 Taiwan New Era AI Robot Inc AI R&D Dept Taipei 105 Taiwan Natl Taiwan Univ Coll Med Dept Anesthesiol Taipei 100 Taiwan Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

关键词： photoplethysmography continuous arterial blood pressure systolic blood pressure diastolic blood pressure deep convolutional autoencoder genetic algorithm

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Bridging deep convolutional autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs

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MATHEMATICAL GEOSCIENCES 2022年第5期54卷 903-939页

作者： Sebacher, Bogdan Toma, Stefan Adrian Mil Tech Acad Ferdinand I Bucharest Romania

One of the main problems associated with applying data assimilation methods for facies models is the lack of geological plausibility in updates. This issue is even more acute for channelized reservoirs, knowing that, without a reliable parameterization, the geometrical structure of the channels can hardly be reproduced in the updated step of any data assimilation method. This paper presents a new methodology for estimation and uncertainty quantification of facies fields in channelized reservoirs, bridging a deep convolutional autoencoder with an ensemble-based method. The proposed methodology is suitable for any geological simulation model and does not use the resampling from the training image when using a multipoint geostatistical simulation model. Besides the channel estimation, the proposed methodology preserves the geological plausibility in the updated step of the history-matching method. The new methodology employs, inside the parameterization, a deep convolutional autoencoder to reconstruct the channel geometry. The convolutional autoencoder is used for image reconstruction purposes. The input of the training set of the autoencoder consists of images (facies fields) generated with a parameterization of the facies fields and perturbed with a Gaussian noise having spatial correlation. This procedure ensures the consistency of the method in the sense that the input fields have a similar structure with the facies fields obtained after the history matching. The methodology is tested for channelized reservoirs, with different levels of complexity, and also by comparison with previous methods that use or not resampling from the training image. The results show an improvement in the geological plausibility, estimation, and uncertainty quantification of the channel distributions while achieving a good data match.

关键词： Channelized reservoirs deep convolutional autoencoder Parameterization Ensemble smoother with multiple data assimilation (ES-MDA)

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