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- W2897260244 abstract "The technology of Multi-view images has been found wide application in 3D reconstruction, surveillance systems and MRI. However, the volume of multi-view data is fairly large and sometimes the time is also limited. In order to cut the data and keep good image quality, we propose a multiple-image pattern low-rank tensor algorithm based on compressed sensing, in which we propose to exploit the model of tensor to find the property of low rank and extend it into multiple-image pattern. In our work, we further propose an efficient algorithm to solve the low-rank tensor method using the truncated HOSVD method and alternative direction multiplier method technique. Experimental results demonstrate that our method outperforms previous methods with almost 1DB higher than the PSNR of existing results." @default.
- W2897260244 created "2018-10-26" @default.
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- W2897260244 date "2018-06-01" @default.
- W2897260244 modified "2023-09-27" @default.
- W2897260244 title "Reconstruction of Compressed Sensed Images with Multiple-Image Pattern Low-Rank Tensor" @default.
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- W2897260244 doi "https://doi.org/10.1109/icivc.2018.8492809" @default.
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