The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Huanfeng Shen - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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hyperspectral image denoising employing a spectral Spatial adaptive total variation model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model
IEEE Transactions on Circuits and Systems for Video Technology, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:Total variation (TV) has been used as a popular and effective image prior model in regularization-based image processing fields, such as denoising, deblurring, super-resolution (SR), and others, because of its ability to preserve edges. However, as the TV model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different Spatial Property regions in the image. In this paper, we propose a Spatially weighted TV image SR algorithm, in which the Spatial information distributed in different image regions is added to constrain the SR process. A newly proposed and effective Spatial information indicator called difference curvature is used to identify the Spatial Property of each pixel, and a weighted parameter determined by the difference curvature information is added to constrain the regularization strength of the TV regularization at each pixel. Meanwhile, a majorization-minimization algorithm is used to optimize the proposed Spatially weighted TV SR model. Finally, a significant amount of simulated and real data experimental results show that the proposed Spatially weighted TV SR algorithm not only efficiently reduces the “artifacts” produced with a TV model in fat regions of the image, but also preserves the edge information, and the reconstruction results are less sensitive to the regularization parameters than the TV model, because of the consideration of the Spatial information constraint.
Qiangqiang Yuan - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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hyperspectral image denoising employing a spectral Spatial adaptive total variation model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model
IEEE Transactions on Circuits and Systems for Video Technology, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:Total variation (TV) has been used as a popular and effective image prior model in regularization-based image processing fields, such as denoising, deblurring, super-resolution (SR), and others, because of its ability to preserve edges. However, as the TV model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different Spatial Property regions in the image. In this paper, we propose a Spatially weighted TV image SR algorithm, in which the Spatial information distributed in different image regions is added to constrain the SR process. A newly proposed and effective Spatial information indicator called difference curvature is used to identify the Spatial Property of each pixel, and a weighted parameter determined by the difference curvature information is added to constrain the regularization strength of the TV regularization at each pixel. Meanwhile, a majorization-minimization algorithm is used to optimize the proposed Spatially weighted TV SR model. Finally, a significant amount of simulated and real data experimental results show that the proposed Spatially weighted TV SR algorithm not only efficiently reduces the “artifacts” produced with a TV model in fat regions of the image, but also preserves the edge information, and the reconstruction results are less sensitive to the regularization parameters than the TV model, because of the consideration of the Spatial information constraint.
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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Spectral-Spatial clustering of hyperspectral remote sensing image with sparse subspace clustering model
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015Co-Authors: Han Zhai, Liangpei Zhang, Hongyan Zhang, Pingxiang Li, Xiong XuAbstract:Clustering for hyperspectral imagery (HSI) is a very challenging task due to its inherent spectral and Spatial complexity. In this paper, we propose a novel spectral-Spatial sparse subspace clustering (S4C) algorithm for hyperspectral imagery. Firstly, by treating each kind of ground class as a subspace, we introduce sparse subspace clustering (SSC) algorithm to HSIs. Then considering the spectral and Spatial Property of HSI, the high spectral correlation and rich Spatial information of the HSIs are taken into consideration in the sparse subspace clustering model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Lastly, spectral clustering is applied to the adjacent matrix to obtain the final image clustering result. Several experiments were conducted to illustrate the performance of the proposed algorithm.
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Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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hyperspectral image denoising employing a spectral Spatial adaptive total variation model
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different Spatial Property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different Spatial Property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-Spatial adaptive total variation (TV) model, in which the spectral noise differences and Spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-Spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-Spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
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Multiframe Super-Resolution Employing a Spatially Weighted Total Variation Model
IEEE Transactions on Circuits and Systems for Video Technology, 2012Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng ShenAbstract:Total variation (TV) has been used as a popular and effective image prior model in regularization-based image processing fields, such as denoising, deblurring, super-resolution (SR), and others, because of its ability to preserve edges. However, as the TV model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different Spatial Property regions in the image. In this paper, we propose a Spatially weighted TV image SR algorithm, in which the Spatial information distributed in different image regions is added to constrain the SR process. A newly proposed and effective Spatial information indicator called difference curvature is used to identify the Spatial Property of each pixel, and a weighted parameter determined by the difference curvature information is added to constrain the regularization strength of the TV regularization at each pixel. Meanwhile, a majorization-minimization algorithm is used to optimize the proposed Spatially weighted TV SR model. Finally, a significant amount of simulated and real data experimental results show that the proposed Spatially weighted TV SR algorithm not only efficiently reduces the “artifacts” produced with a TV model in fat regions of the image, but also preserves the edge information, and the reconstruction results are less sensitive to the regularization parameters than the TV model, because of the consideration of the Spatial information constraint.
Christopher Costello - One of the best experts on this subject based on the ideXlab platform.
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PRIVATE CONSERVATION IN TURF-MANAGED FISHERIES
Natural Resource Modeling, 2016Co-Authors: Christopher Costello, Daniel T. KaffineAbstract:Spatial Property rights in the ocean, such as territorial user right fisheries (TURFs), are increasingly used to overcome the tragedy of the commons. TURFs engender vastly different fishing incentives than in a common pool race; while this likely enhances conservation beyond an open access setting, conservation organizations may desire even greater protection. We argue that because TURFs are Property rights, their implementation opens the door for “private conservation,” for example, where a conservation organization would purchase a set of TURFs to create an un-fished marine reserve network. This possibility has interesting implications for biodiversity conservation, fishery management, and economic incentives, yet has received almost no attention in the literature. We examine this issue in a numerical Spatial-dynamic bioeconomic fishery model. Among other novel findings, we show: (i) Private acquisition of TURFs is likely to be a relatively inexpensive marine conservation strategy, particularly if the conservationist can capture some of the “conservation rents” that accrue due to spillover; (ii) Accounting for the strategic response of remaining fishermen can significantly reduce the cost of conservation; and (iii) The degree of fishing cooperation across TURFs plays a pivotal role in the costs of conservation; more-cooperative TURF owners engage in more “free” conservation, dramatically reducing the overall costs of achieving a particular conservation target.
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matching Spatial Property rights fisheries with scales of fish dispersal
Ecological Applications, 2011Co-Authors: Crow White, Christopher CostelloAbstract:Regulation of fisheries using Spatial Property rights can alleviate competition for high-value patches that hinders economic efficiency in quota-based, rights-based, and open-access management programs. However, efficiency gains erode when delineation of Spatial rights constitutes incomplete ownership of the resource, thereby degrading its local value and promoting overexploitation. Incomplete ownership may be particularly prevalent in the Spatial management of mobile fishery species. We developed a game-theoretic bioeconomic model of Spatial Property rights representing territorial user rights fisheries (TURF) management of nearshore marine fish and invertebrate species with mobile adult and larval life history stages. Strategic responses by fisheries in neighboring management units result in overexploitation of the stock and reduced yields for each fishery compared with those attainable without resource mobility or with coordination or sole control in fishing effort. High dispersal potential of the larval stage, a common trait among nearshore fishery species, coupled with scaling of management units to only capture adult mobility, a common characteristic of many nearshore TURF programs, in particular substantially reduced stock levels and yields. In a case study of hypothetical TURF programs of nearshore fish and invertebrate species, management units needed to be tens of kilometers in alongshore length to minimize larval export and generate reasonable returns to fisheries. Cooperation and quota regulations represent solutions to the problem that need to be quantified in cost and integrated into the determination of the acceptability of Spatial Property rights management of fisheries.
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Unitization of Spatially connected renewable resources
The B.E. Journal of Economic Analysis & Policy, 2011Co-Authors: Daniel T. Kaffine, Christopher CostelloAbstract:Spatial connectivity of renewable resources induces a Spatial externality in extraction. We explore the consequences of decentralized Spatial Property rights in the presence of Spatial externalities. We generalize the notion of unitization—developed to enhance cooperative extraction of oil and gas fields—and apply it to renewable resources which face a similar Spatial commons problem. We find that unitizing a common pool renewable resource can yield first-best outcomes even when participation is voluntary, provided profit sharing rules can vary by participant.
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marine protected areas in Spatial Property rights fisheries
Australian Journal of Agricultural and Resource Economics, 2010Co-Authors: Christopher Costello, Daniel T. KaffineAbstract:Marine protected areas (MPAs) and Spatial Property rights (TURFs) are two seemingly contradictory approaches advocated as solutions to common Property failures in fisheries. MPAs limit harvest to certain areas, but may enhance profits outside via spillover. TURFs incentivize local stewardship but may be plagued by Spatial externalities when the TURF size is insufficient to capture all dispersal. Within a numerical model parameterized to a California marine species, we explore the economic and ecological effects of imposing MPAs on a TURF-regulated fishery. Whether MPAs can enhance or diminish profits (or fish abundance) hinges critically on the level of coordination already occurring between TURF owners. If coordination is complete, private MPAs may already emerge in some TURFs; implementing additional MPAs reduces profits. However, to the extent that coordination is incomplete, strategically sited MPAs may be an effective complement to Spatial Property rights-based fisheries, increasing both fishery profits and abundance.
Linwei Wang - One of the best experts on this subject based on the ideXlab platform.
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Multiple-model Bayesian approach to volumetric imaging of cardiac current sources
2014 IEEE International Conference on Image Processing (ICIP), 2014Co-Authors: Azar Rahimi, Jingjia Xu, Linwei WangAbstract:Noninvasive cardiac electrophysiological imaging aims to mathematically reconstruct the spatio-temporal dynamics of cardiac current sources from body-surface electrocardiography data. This ill-posed problem is often regularized by imposing a certain constraining model on the solution. However, it enforces the source distribution to follow a pre-assumed Spatial structure that does not always match the spatio-temporal changes of current sources. We propose a Bayesian approach for 3D current source estimation that consists of a continuous combination of multiple models, each reflecting a specific Spatial Property for current sources. Multiple models are incorporated into our Bayesian approach as an Lp-norm prior for current sources, where p is an unknown hyperparameter with prior probabilistic distribution defined over the range between 1 and 2. The current source estimation is then obtained as an optimally weighted combination of solutions across all models, the weight being determined from posterior distribution of p inferred from electrocardiography data. The performance of our proposed approach is assessed in a set of synthetic and real-data experiments on human heart-torso models. While the use of fixed models such as L1- and L2-norm only properly recovers sources with specific Spatial structures, our method delivers consistent performance in reconstructing sources with different extents and structures.
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Noninvasive Transmural Electrophysiological Imaging Based on Minimization of Total-Variation Functional
IEEE Transactions on Medical Imaging, 2014Co-Authors: Jingjia Xu, Azar Rahimi Dehaghani, Linwei WangAbstract:While tomographic imaging of cardiac structure and kinetics has improved substantially, electrophysiological mapping of the heart is still restricted to the surface with little or no depth information beneath. The progress in reconstructing 3-D action potential from surface voltage data has been hindered by the intrinsic ill-posedness of the problem and the lack of a unique solution in the absence of prior assumptions. In this work, we propose a novel adaption of the total-variation (TV) prior to exploit the unique Spatial Property of transmural action potential of being piecewise smooth with a steep boundary (gradient) separating depolarized and repolarized regions. We present a variational TV-prior instead of a common discrete TV-prior for improved robustness to mesh resolution, and solve the TV-minimization by a sequence of weighted, first-order L2-norm minimization. In a large set of phantom experiments, the proposed method is shown to outperform existing quadratic methods in preserving the steep gradient of action potential along the border of infarcts, as well as in capturing the disruption to the normal path of electrical wavefronts. Real-data experiments also further demonstrate the potential of the proposed method in revealing the location and shape of infarcts when quadratic methods fail to do so.
