scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals A NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES

2016 ◽  
Vol III-6 ◽  
pp. 57-61 ◽  
Author(s):  
Xinxin Liu ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Liangpei Zhang ◽  
Qing Cheng
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Model Optimization ◽  
Remote Sensing Images ◽  
Qualitative And Quantitative ◽  
Statistical Property ◽  
Alternating Direction ◽  
Proposed Model ◽  
The Common ◽  
Reliable Solution

In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.

scholarly journals A NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES

2016 ◽  
Vol III-6 ◽  
pp. 57-61
Author(s):  
Xinxin Liu ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Liangpei Zhang ◽  
Qing Cheng
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Model Optimization ◽  
Remote Sensing Images ◽  
Qualitative And Quantitative ◽  
Statistical Property ◽  
Alternating Direction ◽  
Proposed Model ◽  
The Common ◽  
Reliable Solution

In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.

scholarly journals Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2985 ◽  
Author(s):  
Yeneng Lin ◽  
Dongyun Xu ◽  
Nan Wang ◽  
Zhou Shi ◽  
Qiuxiao Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Network Models ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Proposed Model ◽  
Wide Range ◽  
Network Modules ◽  
Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

scholarly journals Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianguang Zhu ◽  
Kai Li ◽  
Binbin Hao
Keyword(s):  
Total Variation ◽  
High Order ◽  
Variational Model ◽  
Total Variation Regularization ◽  
Current State ◽  
Model Combining ◽  
Alternating Direction ◽  
Proposed Model ◽  
Sharp Edges ◽  
Thresholding Algorithm

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.

scholarly journals A Convex Optimization Model and Algorithm for Retinex

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qing-Nan Zhao ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma ◽  
Ming-Hui Cheng
Keyword(s):  
Convex Optimization ◽  
Optimization Model ◽  
Main Idea ◽  
Data Fitting ◽  
Input Image ◽  
Alternating Direction Method ◽  
Total Variation Regularization ◽  
Alternating Direction ◽  
Proposed Model ◽  
Reflection Component

Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM) to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.

scholarly journals Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model

2021 ◽  
Vol 13 (10) ◽  
pp. 5391
Author(s):  
Yinsheng Yang ◽  
Gang Yuan ◽  
Jiaxiang Cai ◽  
Silin Wei
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Models ◽  
Hidden Markov ◽  
Real Data ◽  
Gaussian Mixture ◽  
Waste Generation ◽  
Time Data ◽  
Proposed Model ◽  
Comparison Results

Disassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation.

scholarly journals Double Regression-Based Sparse Unmixing for Hyperspectral Images

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shuaiyang Zhang ◽  
Wenshen Hua ◽  
Gang Li ◽  
Jie Liu ◽  
Fuyu Huang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Clustering Algorithm ◽  
Real Data ◽  
Alternating Direction Method ◽  
Threshold Function ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Sparsity Constraints ◽  
The Cost ◽  
Unmixing Model

Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression-based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression-based sparse unmixing via K -means ( DRSU M K − means ) is proposed. The improved K -means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l 2 , 0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row-hard-threshold function to solve the l 2 , 0 norm directly. Then, DRSU M K − means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSU M K − means .

scholarly journals Robust Tensor PCA based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Rakesh R Kamath ◽  
hahn choo ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Moving Objects ◽  
Real Data ◽  
Video Data ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Melt Pool ◽  
Alternating Direction ◽  
Spatio Temporal ◽  
Static Background

<div>Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse RTPCA (SS-RTPCA) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatio-temporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the `1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.</div>

scholarly journals Robust Tensor PCA based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Rakesh R Kamath ◽  
hahn choo ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Moving Objects ◽  
Real Data ◽  
Video Data ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Melt Pool ◽  
Alternating Direction ◽  
Spatio Temporal ◽  
Static Background

<div>Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse RTPCA (SS-RTPCA) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatio-temporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the `1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.</div>

THE DEVELOPMENT OF THE MATHEMATICAL MODEL OF PREDICTING THE INDICATORS OF ACCREDITATION OF TECHNICAL UNIVERSITIES IN THE RUSSIAN FEDERATION

2017 ◽  
pp. 27-38
Author(s):  
Olga Mikhaylovna Tikhonova ◽  
Alexander Fedorovich Rezchikov ◽  
Vladimir Andreevich Ivashchenko ◽  
Vadim Alekseevich Kushnikov
Keyword(s):  
Mathematical Model ◽  
System Dynamics ◽  
Regression Model ◽  
Oriented Graph ◽  
Real Data ◽  
Educational Process ◽  
Proposed Model ◽  
Linear Differential ◽  
The University ◽  
The Mathematical Model

The paper presents the system of predicting the indicators of accreditation of technical universities based on J. Forrester mechanism of system dynamics. According to analysis of cause-and-effect relationships between selected variables of the system (indicators of accreditation of the university) there was built the oriented graph. The complex of mathematical models developed to control the quality of training engineers in Russian higher educational institutions is based on this graph. The article presents an algorithm for constructing a model using one of the simulated variables as an example. The model is a system of non-linear differential equations, the modelling characteristics of the educational process being determined according to the solution of this system. The proposed algorithm for calculating these indicators is based on the system dynamics model and the regression model. The mathematical model is constructed on the basis of the model of system dynamics, which is further tested for compliance with real data using the regression model. The regression model is built on the available statistical data accumulated during the period of the university's work. The proposed approach is aimed at solving complex problems of managing the educational process in universities. The structure of the proposed model repeats the structure of cause-effect relationships in the system, and also provides the person responsible for managing quality control with the ability to quickly and adequately assess the performance of the system.

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