- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Low-dose single-energy material decomposition in radiography using a sparse-view computed tomography scan
摘要： Dual-energy material decomposition (DEMD) is a well-established theoretical x-ray technique that uses low- and high-kilovoltage radiographs to separate soft tissue and bone in radiography and computed tomography (CT). However, it requires double exposures that result in increased patient radiation doses, causes increases in the execution time, and generates errors due to misregistration attributed to the patient motion between two scans. In this study, we investigated a low-dose, single-energy material decomposition (LSEMD) method in radiography using a sparse-view computed tomography scan where the attenuation length in the object was estimated from the CT image. We performed a systematic simulation and an experiment to demonstrate the feasibility of use of the LSEMD method in radiography. Only 60 projections, far fewer than those required by the Nyquist sampling theory, were acquired at an x-ray tube voltage of 80 kVp, and were used to reconstruct a sparse-view CT image with a state-of-the-art dictionary-learning (DL) algorithm. We investigated the image performance of the LSEMD and compared the elicited results with those obtained with the use of DEMD (80 kVp and 120 kVp were used). Our results indicate that the DL algorithm produced high-quality sparse-view CT images. Accordingly, the LSEMD method yielded material decomposition results that were very similar to the results elicited by the conventional DEMD method in radiography.
关键词： dictionary-learning,low-dose single-energy material decomposition,Computed tomography,dual-energy material decomposition
更新于2025-09-19 17:15:36
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Coordinate-free Formulation and Evaluation of Tensor Greena??s Functions for General Homogeneous Uniaxial Anisotropic Media
摘要： Stained glass windows are designed to reveal their powerful artistry under diverse and time-varying lighting conditions; virtual relighting of stained glass, therefore, represents an exceptional tool for the appreciation of this age old art form. However, as opposed to most other artifacts, stained glass windows are extremely difficult if not impossible to analyze using controlled illumination because of their size and position. In this paper, we present novel methods built upon image based priors to perform virtual relighting of stained glass artwork by acquiring the actual light transport properties of a given artifact. In a preprocessing step, we build a material-dependent dictionary for light transport by studying the scattering properties of glass samples in a laboratory setup. We can now use the dictionary to recover a light transport matrix in two ways: under controlled illuminations the dictionary constitutes a sparsifying basis for a compressive sensing acquisition, while in the case of uncontrolled illuminations the dictionary is used to perform sparse regularization. The proposed basis preserves volume impurities and we show that the retrieved light transport matrix is heterogeneous, as in the case of real world objects. We present the rendering results of several stained glass artifacts, including the Rose Window of the Cathedral of Lausanne, digitized using the presented methods.
关键词： light transport,recovery,dictionary learning,Banded matrices,stained glass,sparse cultural artifacts,computational relighting
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Human Cardiac Tissue Collagen Polarity Revealed Using Polarimetric Second-Harmonic Generation Microscopy
摘要： Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, ?lm production, and medical rehabilita- tion. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high com- plexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal pat- terns and the structural sparsity embedded in motion data. We ?rst replace the regularly used entire pose model with a much ?ne-grained partlet model as feature representation to exploit the abundant local body part posture and movement similari- ties. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution informa- tion and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
关键词： Microsoft Kinect,robust structured sparse coding,Human motion denoising,motion capture data,robust dictionary learning,(cid:2)2,p-norm
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Nanocomposites Polymer/Gold Nanoparticles/Chlorin e6 for Antitumor Laser Medicine
摘要： Stained glass windows are designed to reveal their powerful artistry under diverse and time-varying lighting conditions; virtual relighting of stained glass, therefore, represents an exceptional tool for the appreciation of this age old art form. However, as opposed to most other artifacts, stained glass windows are extremely difficult if not impossible to analyze using controlled illumination because of their size and position. In this paper, we present novel methods built upon image based priors to perform virtual relighting of stained glass artwork by acquiring the actual light transport properties of a given artifact. In a preprocessing step, we build a material-dependent dictionary for light transport by studying the scattering properties of glass samples in a laboratory setup. We can now use the dictionary to recover a light transport matrix in two ways: under controlled illuminations the dictionary constitutes a sparsifying basis for a compressive sensing acquisition, while in the case of uncontrolled illuminations the dictionary is used to perform sparse regularization. The proposed basis preserves volume impurities and we show that the retrieved light transport matrix is heterogeneous, as in the case of real world objects. We present the rendering results of several stained glass artifacts, including the Rose Window of the Cathedral of Lausanne, digitized using the presented methods.
关键词： computational relighting,stained glass,recovery,Banded matrices,light transport,dictionary learning,sparse cultural artifacts
更新于2025-09-19 17:13:59
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Deep Coupled ISTA Network for Multi-modal Image Super-Resolution
摘要： Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find the high-resolution (HR) version of this image with the guidance of an HR image from another modality. In this paper, we use a model-based approach to design a new deep network architecture for MISR. We first introduce a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency. In JMDL, we simultaneously learn three dictionaries and two transform matrices to combine the modalities. Then, by unfolding the iterative shrinkage and thresholding algorithm (ISTA), we turn the JMDL model into a deep neural network, called deep coupled ISTA network. Since the network initialization plays an important role in deep network training, we further propose a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy. Specifically, we model the network initialization as a multi-layer dictionary learning problem, and solve it through convex optimization. The proposed LOA is demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. Finally, we compare our method with other state-of-the-art methods in the MISR task. The numerical results show that our method consistently outperforms others both quantitatively and qualitatively at different upscaling factors for various multi-modal scenarios.
关键词： ISTA,multi-modal image super-resolution,dictionary learning,deep neural network
更新于2025-09-16 10:30:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Accurate Dictionary Learning with Direct Sparsity Control
摘要： Dictionary learning is a popular method for obtaining sparse linear representations for high dimensional data, with many applications in image classification, signal processing and machine learning. In this paper, we introduce a novel dictionary learning method based on a recent variable selection algorithm called Feature Selection with Annealing (FSA). Because FSA uses an L0 constraint instead of the L1 penalty, it does not introduce any bias in the coefficients and obtains a more accurate sparse representation. Furthermore, the L0 constraint makes it easy to directly specify the desired sparsity level instead of indirectly through a L1 penalty. Finally, experimental validation on real gray-scale images shows that the proposed method obtains higher accuracy and efficiency in dictionary learning compared to classical methods based on the L1 penalty.
关键词： dictionary learning,LARS,sparse coding,FSA
更新于2025-09-11 14:15:04
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Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
摘要： In recent studies in hyperspectral imaging, biometrics, and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data are limited; therefore, hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence suboptimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.
关键词： hyperspectral imaging,dictionary learning,Classification,deep learning,supervised learning
更新于2025-09-10 09:29:36
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Morphology-based visible-infrared image fusion framework for smart city
摘要： Sparse representation-based approaches are often applied to image fusion. Owing to the difficulties of obtaining a complete and non-redundant dictionary, this paper proposes a hierarchical image fusion framework that applies layer-by-layer deep learning techniques to explore the detailed information of images and extract key information of images for dictionary learning. According to morphological similarities, this paper clusters source image patches into smooth, stochastic, and dominant orientation patch group. High-frequency and low-frequency components of three clustered image-patch groups are fused by max-L1 and L2-norm based weighted average fusion rule respectively. The fused low-frequency and high-frequency components are combined to obtain the final fusion results. The comparison experimentations confirm the feasibility and effectiveness of the proposed image fusion solution.
关键词： sparse representation,image fusion,geometric information classification,dictionary learning,smart city
更新于2025-09-10 09:29:36
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Sparse Dictionary Learning for Blind Hyperspectral Unmixing
摘要： Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity. To solve this problem, this paper proposes an efficient algorithm to find all paths of the 1-regularization problem and select the best set of variables for the final abundances estimation. Based on the proposed algorithm, a DL framework is designed for hyperspectral unmixing. Our experimental results indicate that our method performs much better than conventional methods in terms of DL and hyperspectral data reconstruction. More importantly, it alleviates the difficulty of prescribing the sparsity.
关键词： sparse coding,Dictionary learning (DL),hyperspectral unmixing,1-regularization,path algorithm
更新于2025-09-09 09:28:46
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Fusion of United Sparse Principal Component Analysis Dictionary Based on Linear Unmixing Image Technique
摘要： Based on the linear unmixing images of different surface objects, online dictionary learning algorithm was utilized to compute the sparse dictionaries for multispectral linear unmixing images and panchromatic images. Principal component analysis (PCA) was then utilized to generate united sparse PCA dictionaries through the extraction of the first principal components of panchromatic images and unmixing image dictionaries. The number of dictionaries is determined to be 480 after taking into consideration of the limitation in computing power and root-mean-square error of restructured images. Based on these dictionaries, orthogonal matching pursuit method was utilized to calculate the sparse coefficients of multispectral and panchromatic images, separately, while nonnegative matrix factorization fusion algorithm was utilized to calculate multispectral and panchromatic sparse coefficients to obtain sparse coefficient of the fusional image on all bands, with the resulted matrix having a size of 480 × 255 025. These united sparse PCA dictionaries and fusion sparse coefficients were then used to reconstruct the fusional image. Through the analysis of five quantitative indices of fusion assessment, the proposed fusion algorithm has retained the multispectral information of images and enhanced the detailed information in image texture.
关键词： nonnegative matrix factorization (NMF) fusion,principal component analysis (PCA) dictionary,Linear unmixing,orthogonal matching pursuit (OMP) algorithm,online dictionary learning (ODL) algorithm
更新于2025-09-04 15:30:14