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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Published in:2017 IEEE International Conference on Image Processing (ICIP) pp. 2104 - 2108
Main Authors: Lu, Hongyang, Li, Sanqian, Liu, Qiegen, Wang, Yuhao
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2017
Subjects:
HOSVD decomposition
Image restoration
Indexes
Low-rank tensor coding
Multi-filters
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.
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
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  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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StartPage 2104
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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