DocumentCode :
702552
Title :
Truncated and smoothed schatten-p function for robust tensor recovery
Author :
Al-Qizwini, Mohammed ; Radha, Hayder
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
fYear :
2015
fDate :
18-20 March 2015
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, we consider the robust tensor recovery problem in which we recover the low rank and sparse tensors from an observed data that is formed by the superposition of the two tensors. Our main contribution in this paper is deriving the truncated and smoothed schatten-p function to solve the robust tensor recovery problem using the Augmented Lagrangian Multiplier (ALM) optimization algorithm. Further, we compare the performance of our proposed algorithm against state-of-the-art robust tensor recovery algorithms using a variety of corrupted image and video signals. The experimental results show that our algorithm achieves an average of 2 dB performance improvement in PSNR; and at the same time it needs less number of iterations to converge when compared to state-of-the-art algorithms that tackle this problem.
Keywords :
image processing; optimisation; tensors; ALM optimization algorithm; PSNR; augmented Lagrangian multiplier optimization algorithm; corrupted image; robust tensor recovery algorithms; robust tensor recovery problem; smoothed Schatten-p function; sparse tensors; video signals; Algorithm design and analysis; Computational modeling; Optimization; PSNR; Robustness; Sparse matrices; Tensile stress; Augmented Lagrange Multiplier; Smoothed Schatten-p Functions; Tensor Recovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems (CISS), 2015 49th Annual Conference on
Conference_Location :
Baltimore, MD
Type :
conf
DOI :
10.1109/CISS.2015.7086815
Filename :
7086815
Link To Document :
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