- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe, Japan (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - A Novel Approach to Underwater De-Scattering Based on Sparse and Low-Rank Matrix Decomposition
摘要： The images captured in turbid water suffer from severe visual degradation due to the dramatic scattering effects caused by abundant large suspended particles. This paper propose a novel underwater image de-scattering approach with low complexity, which can separate the scattering component from the image by using the sparse and low-rank matrix decomposition. The experimental results show that the proposed approach can enhance the global image contrast effectively. Additionally, this approach does not require specialized hardware or prior knowledge about the underwater environment; it is suitable for the underwater imaging system in real time.
关键词： turbid water,low-rank matrix decomposition,de-scattering,underwater image
更新于2025-09-23 15:23:52
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Iterative Adaptive Nonconvex Low-Rank Tensor Approximation to Image Restoration Based on ADMM
摘要： In this paper, in order to recover more ?ner details of the image and to avoid the loss of image structure information for image restoration problem, we develop an iterative adaptive weighted core tensor thresholding (IAWCTT) approach based on the alternating direction method of multipliers (ADMM). By observing the decoupling property of the ADMM algorithm, we ?rst propose that the key step to image restoration is to tackle the denoising subproblem ef?ciently using appropriate prior information. Secondly, by analyzing the properties of the core tensor, we propose that low-rank tensor approximation can be implemented by penalizing the core tensor itself, instead of penalizing the CP rank, Tucker rank or the multilinear rank and Tubal rank. The IAWCTT approach is proposed to solve the denoising subproblem in the ADMM framework, and we claim that such an adaptive weighted scheme is equivalent to a kind of nonconvex penalty for the core tensor; thus, it is unnecessary to use the nonconvex penalty term to induce strong sparse/low-rank solution in image restoration optimization problem, because the scheme that selecting appropriate weights to the convex penalty term can also lead to strong sparse/low-rank solution. Numerical experiments show that our proposed model and algorithm are comparable to other state-of-the-art models and methods.
关键词： Image restoration,Low-rank tensor approximation,ADMM,Nonconvex penalty
更新于2025-09-23 15:23:52
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[IEEE 2018 Chinese Control And Decision Conference (CCDC) - Shenyang (2018.6.9-2018.6.11)] 2018 Chinese Control And Decision Conference (CCDC) - Optic disc segmentation method based on low rank matrix recovery theory
摘要： Optic disc(OD) detection and segmentation is one of the key elements for automatic retinal disease screening systems. This paper proposes an optic disc segmentation algorithm. The unified method based on low rank matrix recovery theory is used to make a significant detection. Then Hough transform is combined to obtain the final segmentation results. The proposed algorithm has short computation time and low complexity. It is robust to variable image illuminations and angles. The algorithm achieves the OD segmentation accuracy of 92.9% of the images on the MESSIDOR public dataset.
关键词： Optic disc,Hough transform,Segmentation,Low rank matrix recovery
更新于2025-09-23 15:23:52
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Low-rank and sparse matrix decomposition with background position estimation for hyperspectral anomaly detection
摘要： Hyperspectral anomaly detection (AD) has attracted much attention over the last 20 years. It distinguishes pixels with significant spectral differences from the background without any prior knowledge. The low-rank and sparse matrix decomposition (LRaSMD)-based detector has been applied to AD, where the anomaly value is measured by Euclidean distance based on the sparse component. However, the background interference in sparse component seriously increases the false alarm rate and influences the detection of real anomalies. In this paper, a novel AD method based on LRaSMD and background position estimation is proposed, which aims to suppress background interference in the sparse component for a better separation between background and anomalies. Firstly, the original sparse matrix is obtained using the traditional LRaSMD method. Secondly, the abundance maps are constructed by the sequential maximum angel convex cone (SMACC) endmember extraction model. Thirdly, considering that the anomalies occupy only a few pixels with a low probability, the coordinate positions of background pixels are estimated through these abundance maps. Finally, the spectra corresponding to these positions in the original sparse matrix are replaced with zero vectors, and the final anomaly value is calculated based on the improved sparse matrix. The proposed method achieves an outstanding performance by considering both the spectral and spatial characteristics of anomalies. Experimental results on synthetic and real-world hyperspectral datasets demonstrate the superiority of the proposed method compared with several state-of-the-art AD detectors.
关键词： Anomaly detection,Background estimation,Low-rank and sparse matrix decomposition,Hyperspectral imagery,Endmember extraction
更新于2025-09-23 15:23:52
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Quaternion-based weighted nuclear norm minimization for color image denoising
摘要： The quaternion method plays an important role in color image processing, because it represents the color image as a whole rather than as a separate color space component, thus naturally handling the coupling among color channels. The weighted nuclear norm minimization (WNNM) scheme assigns different weights to different singular values, leading to more reasonable image representation method. In this paper, we propose a novel quaternion weighted nuclear norm minimization (QWNNM) model and algorithm under the low rank sparse framework. The proposed model represents the color image as a low rank quaternion matrix, where quaternion singular value decomposition can be calculated by its equivalent complex matrix. We solve the QWNNM by adaptively assigning different singular values with different weights. Color image denoising is implemented by QWNNM based on non-local similarity priors. In this new color space, the inherent color structure can be well preserved during image reconstruction. For high noise levels, we apply a Gaussian lowpass filter (LPF) to the noisy image as a preprocessing before QWNNM, which reduces the iteration numbers and improves the denoised results. The experimental results clearly show that the proposed method outperforms K-SVD, QKSVD and WNNM in terms of both quantitative criteria and visual perceptual.
关键词： Quaternion singular value decomposition,Non-local similarity priors,Quaternion weighted nuclear norm minimization,Low rank,Color image denoising
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection
摘要： This paper presents a novel method for hyperspectral anomaly detection considering the spectral redundancy and exploiting spectral-spatial information at the same time. We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize spectral-spatial information, the cubic data (two spatial dimensions and one spectral dimension) is treated as a 3-order tensor. Then the non-local self-similarity is fully explored in our method. For the reason to reduce the ringing artifacts caused by over-lapped segmentation in exploring the non-local self-similarity, we introduce the hyper-Laplacian constrained low-rank tensor decomposition and we get the separated background and residual parts. Finally, to eliminate the effect of Gaussian noise, we use local-RX basic detector to detect the residual matrix. Experimental results on two real hyperspectral data sets verified the effectiveness of the proposed algorithms for HSI anomaly detection.
关键词： low-rank tensor decomposition,hyperspectral anomaly detection,Dimensionality reduction
更新于2025-09-23 15:23:52
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Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation
摘要： Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.
关键词： ADMM,total variation,hyperspectral mixed denoising,low-rank approximation,spectral difference space
更新于2025-09-23 15:23:52
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Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification
摘要： Most of the available graph-based semisupervised hyperspectral image classification methods adopt the cluster assumption to construct a Laplacian regularizer. However, they sometimes fail due to the existence of mixed pixels whose recorded spectra are a combination of several materials. In this paper, we propose a geometric low-rank Laplacian regularized semisupervised classifier, by exploring both the global spectral geometric structure and local spatial geometric structure of hyperspectral data. A new geometric regularized Laplacian low-rank representation (GLapLRR)-based graph is developed to evaluate spectral-spatial affinity of mixed pixels. By revealing the global low-rank and local spatial structure of images via GLapLRR, the constructed graph has the characteristics of spatial–spectral geometry description, robustness, and low sparsity, from which a more accurate classification of mixed pixels can be achieved. The proposed method is experimentally evaluated on three real hyperspectral datasets, and the results show that the proposed method outperforms its counterparts, when only a small number of labeled instances are available.
关键词： Dual geometric low-rank structure,mixed pixels,spectral-spatial affinity,hyperspectral image classification (HIC),support vector machine,semisupervised
更新于2025-09-23 15:23:52
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Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization
摘要： Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre-and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.
关键词： Denoising,Computed tomography image,Tensor low rank recovery,Tensor total variation
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sparse and Smooth Feature Extraction for Hyperspectral Imagery
摘要： In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.
关键词： regularization,Feature extraction,sparsity,low-rank model,total variation,hyperspectral image
更新于2025-09-23 15:22:29