Results 21 to 30 of about 1,596,426 (191)
Sparse-View Ct Reconstruction Via Convolutional Sparse Coding [PDF]
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications.
Bao, Peng +4 more
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NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022 This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction (Cone Beam Computed Tomography) that requires no external training data.
Ruyi Zha, Yanhao Zhang, Hongdong Li
semanticscholar +1 more source
Sparse signal reconstruction by swarm intelligence algorithms
Engineering Science and Technology, an International Journal, 2021 This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l1-minimization methods. The proposed method employs swarm intelligence based
Murat Emre Erkoç, Nurhan Karaboğa
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Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023 Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and ...
Yiqun Lin, Zhongjin Luo, Wei Zhao, X. Li
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Sparse Image Reconstruction for Molecular Imaging [PDF]
IEEE Transactions on Image Processing, 2009 12 pages, 8 ...
Ting, Michael +2 more
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Measurement, 2021 In this paper, the performance of the sparse reconstruction (SR) and the delay-and-sun (DAS) methods for damage localization, were evaluated for various environmental and operational conditions, both numerically and experimentally. To assess these damage
A. Nokhbatolfoghahai +2 more
semanticscholar +1 more source
Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Data-Centric Engineering, 2021 Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows.
D. Carter +3 more
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Color Image Super-resolution Reconstruction Based on Color Constraint and Nonlocal Sparse Representation [PDF]
Jisuanji gongcheng, 2019 Color image super-resolution reconstruction method based on sparse representation model usually adopts sparse coding process based on image blocks,which easily leads to the instability of sparse representation,and the problems of detail blurring and ...
XU Zhigang, MA Qiang, ZHU Honglei, ZHANG Moyi
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Remote Sensing, 2019 The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference.
Xiaokang Yao +4 more
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Adaptive thresholding for sparse image reconstruction
Telfor Journal, 2023 The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method
Volarić, Ivan, Sučić, Viktor
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