The Experts below are selected from a list of 84300 Experts worldwide ranked by ideXlab platform
Wei Wei - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral image super resolution extending an effective fusion based method without knowing the spatial transformation matrix
International Conference on Multimedia and Expo, 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.
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ICME - Hyperspectral image super-resolution extending: An effective fusion based method without knowing the spatial transformation matrix
2017 IEEE International Conference on Multimedia and Expo (ICME), 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral image super resolution extending an effective fusion based method without knowing the spatial transformation matrix
International Conference on Multimedia and Expo, 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.
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ICME - Hyperspectral image super-resolution extending: An effective fusion based method without knowing the spatial transformation matrix
2017 IEEE International Conference on Multimedia and Expo (ICME), 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.
Yu An - One of the best experts on this subject based on the ideXlab platform.
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compactly supported radial basis function based meshless method for photon propagation model of fluorescence molecular tomography
IEEE Transactions on Medical Imaging, 2017Co-Authors: Yu An, Guanglei Zhang, Shixin Jiang, Jinzuo Ye, Jie TianAbstract:Fluorescence Molecular Tomography (FMT) is a powerful imaging modality for the research of cancer diagnosis, disease treatment and drug discovery. Via three-dimensional (3-D) imaging Reconstruction, it can quantitatively and noninvasively obtain the distribution of fluorescent probes in biological tissues. Currently, photon propagation of FMT is conventionally described by the Finite Element Method (FEM), and it can obtain acceptable image quality. However, there are still some inherent inadequacies in FEM, such as time consuming, discretization error and inflexibility in mesh generation, which partly limit its imaging accuracy. To further improve the solving accuracy of photon propagation model (PPM), we propose a novel compactly supported radial basis functions (CSRBFs)-based meshless method (MM) to implement the PPM of FMT. We introduced a series of independent nodes and continuous CSRBFs to interpolate the PPM, which can avoid complicated mesh generation. To analyze the performance of the proposed MM, we carried out numerical heterogeneous mouse simulation to validate the simulated surface fluorescent measurement. Then we performed an in vivo experiment to observe the tomographic Reconstruction. The experimental Results confirmed that our proposed MM could obtain more similar surface fluorescence measurement with the golden standard (Monte-Carlo method), and more accurate Reconstruction Result was achieved via MM in in vivo application.
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meshless Reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function
Journal of Biomedical Optics, 2015Co-Authors: Yu An, Guanglei Zhang, Shixin Jiang, Jinzuo Ye, Wenting Shang, Yang Du, Jie TianAbstract:Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image Reconstruction techniques. Conventional Reconstruction methods based on the finiteelement method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for Reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless Reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The Results suggest that the proposed MM-FMT method can reduce the position error of the Reconstruction Result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jie Tian - One of the best experts on this subject based on the ideXlab platform.
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a fast Reconstruction algorithm for fluorescence molecular tomography via multipath subspace pursuit method
Medical Imaging 2018: Biomedical Applications in Molecular Structural and Functional Imaging, 2018Co-Authors: Haoxuan Ni, Jinzuo Ye, Yang Du, Dehui Xiang, Xinjian Chen, Xiang Deihui, Jie TianAbstract:Fluorescence Molecular Tomography (FMT) is one of the most important preclinical research techniques, which can obtain three-dimensional Reconstruction of tumors in mouse in vivo . However, the ill-posedness of FMT makes its Reconstruction a challenging problem. Therefore, more effective, robust, and accurate Reconstruction methods are needed to be developed to solve the FMT Reconstruction problem. In this paper, a Reconstruction method named multipath subspace pursuit (MSP) is applied to solve the FMT problem. At the end of an iteration, the MSP method creates several candidate support set. Through evaluating the normal of final residual vector, the best candidate can be selected as the final support set. Then the support set is used for reconstructing sense matrix to achieve the goal of FMT Reconstruction. In order to verity the Reconstruction Result of the proposed MSP method, the simulated experiment of triple fluorescent sources and quantitative analyses of position error and relative intensity error for the experiment have been conducted. The MSP method obtains satisfactory Results, and the source position error is below 1 mm. Moreover, the computation time of the MSP method is about one order of magnitude less than iterated shrinkage with the L1-norm (ISlL1) method. The MSP method not only can obtain the Result of robustness but also can reduce the artifacts in the background. The above Results revealed the MSP method for the potential FMT application.
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compactly supported radial basis function based meshless method for photon propagation model of fluorescence molecular tomography
IEEE Transactions on Medical Imaging, 2017Co-Authors: Yu An, Guanglei Zhang, Shixin Jiang, Jinzuo Ye, Jie TianAbstract:Fluorescence Molecular Tomography (FMT) is a powerful imaging modality for the research of cancer diagnosis, disease treatment and drug discovery. Via three-dimensional (3-D) imaging Reconstruction, it can quantitatively and noninvasively obtain the distribution of fluorescent probes in biological tissues. Currently, photon propagation of FMT is conventionally described by the Finite Element Method (FEM), and it can obtain acceptable image quality. However, there are still some inherent inadequacies in FEM, such as time consuming, discretization error and inflexibility in mesh generation, which partly limit its imaging accuracy. To further improve the solving accuracy of photon propagation model (PPM), we propose a novel compactly supported radial basis functions (CSRBFs)-based meshless method (MM) to implement the PPM of FMT. We introduced a series of independent nodes and continuous CSRBFs to interpolate the PPM, which can avoid complicated mesh generation. To analyze the performance of the proposed MM, we carried out numerical heterogeneous mouse simulation to validate the simulated surface fluorescent measurement. Then we performed an in vivo experiment to observe the tomographic Reconstruction. The experimental Results confirmed that our proposed MM could obtain more similar surface fluorescence measurement with the golden standard (Monte-Carlo method), and more accurate Reconstruction Result was achieved via MM in in vivo application.
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meshless Reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function
Journal of Biomedical Optics, 2015Co-Authors: Yu An, Guanglei Zhang, Shixin Jiang, Jinzuo Ye, Wenting Shang, Yang Du, Jie TianAbstract:Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image Reconstruction techniques. Conventional Reconstruction methods based on the finiteelement method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for Reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless Reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The Results suggest that the proposed MM-FMT method can reduce the position error of the Reconstruction Result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Chunna Tian - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral image super resolution extending an effective fusion based method without knowing the spatial transformation matrix
International Conference on Multimedia and Expo, 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.
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ICME - Hyperspectral image super-resolution extending: An effective fusion based method without knowing the spatial transformation matrix
2017 IEEE International Conference on Multimedia and Expo (ICME), 2017Co-Authors: Lei Zhang, Chunna Tian, Chen Ding, Yanning Zhang, Wei WeiAbstract:Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art Result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution Result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate Reconstruction Result compared with other competing methods. Experimental Results demonstrate the effectiveness of the proposed method.