MF-LRTC: Multi-filters guided low-rank tensor coding for image restoration
Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding (MF-LRTC) model for image restoration. The appeal of constructing a low-rank tensor is obvious in many cases for data that naturally comes f...
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| Vydáno v: | 2017 IEEE International Conference on Image Processing (ICIP) s. 2104 - 2108 |
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01.09.2017
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| ISSN: | 2381-8549 |
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| Abstract | Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding (MF-LRTC) model for image restoration. The appeal of constructing a low-rank tensor is obvious in many cases for data that naturally comes from different scales and directions. The MF-LRTC takes advantages of the low-rank tensor coding to capture the sparse convolutional features generated by multi-filters representation. Using such a low-rank tensor coding would reduce the redundancy between feature vectors at neighboring locations and improve the efficiency of the overall sparse representation. In this work, we are committed to achieving this goal by convoluting the target image with filters to formulate multi-features images. Then similarity-grouped cube set extracted from the multi-features images is regarded as a low-rank tensor. The potential effectiveness of this tensor construction strategy is demonstrated in image restoration including image deblurring and compressed sensing (CS) applications. |
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| AbstractList | Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding (MF-LRTC) model for image restoration. The appeal of constructing a low-rank tensor is obvious in many cases for data that naturally comes from different scales and directions. The MF-LRTC takes advantages of the low-rank tensor coding to capture the sparse convolutional features generated by multi-filters representation. Using such a low-rank tensor coding would reduce the redundancy between feature vectors at neighboring locations and improve the efficiency of the overall sparse representation. In this work, we are committed to achieving this goal by convoluting the target image with filters to formulate multi-features images. Then similarity-grouped cube set extracted from the multi-features images is regarded as a low-rank tensor. The potential effectiveness of this tensor construction strategy is demonstrated in image restoration including image deblurring and compressed sensing (CS) applications. |
| Author | Liu, Qiegen Wang, Yuhao Lu, Hongyang Li, Sanqian |
| Author_xml | – sequence: 1 givenname: Hongyang surname: Lu fullname: Lu, Hongyang organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 2 givenname: Sanqian surname: Li fullname: Li, Sanqian organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 3 givenname: Qiegen surname: Liu fullname: Liu, Qiegen organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 4 givenname: Yuhao surname: Wang fullname: Wang, Yuhao organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China |
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| Snippet | Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding... |
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| SubjectTerms | HOSVD decomposition Image restoration Indexes Low-rank tensor coding Multi-filters |
| Title | MF-LRTC: Multi-filters guided low-rank tensor coding for image restoration |
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