Multichannel Image Completion With Mixture Noise: Adaptive Sparse Low-Rank Tensor Subspace Meets Nonlocal Self-Similarity

Xie, Mengying; Liu, Xiaolan; Yang, Xiaowei; Cai, Wenzeng

doi:10.1109/tcyb.2022.3169800

Cited by 2 publications

(2 citation statements)

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“…6 Therefore, data completion and denoising are of interest and have drawn much attention for many years. [7][8][9] In this paper, we focus on data that are represented by a third-order tensor and the observed tensor  ∈ R n 1 ×n 2 ×n 3 is incomplete and noisy. The proposed model for recovering the data is given by min…”

Section: Introductionmentioning

confidence: 99%

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An iterative algorithm for low‐rank tensor completion problem with sparse noise and missing values

Chen,

Huang

2023

Numerical Linear Algebra App

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Robust low‐rank tensor completion plays an important role in multidimensional data analysis against different degradations, such as sparse noise, and missing entries, and has a variety of applications in image processing and computer vision. In this paper, an optimization model for low‐rank tensor completion problems is proposed and a block coordinate descent algorithm is developed to solve this model. It is shown that for one of the subproblems, the closed‐form solution exists and for the other, a Riemannian conjugate gradient algorithm is used. In particular, when all elements are known, that is, no missing values, the block coordinate descent is simplified in the sense that both subproblems have closed‐form solutions. The convergence analysis is established without requiring the latter subproblem to be solved exactly. Numerical experiments illustrate that the proposed model with the algorithm is feasible and effective.

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Section: Introductionmentioning

confidence: 99%

“…However, the real‐world data are usually incomplete and grossly corrupted, such as computed tomography, 3 electroencephalogram (EEG) signals, 4 magnetic resonance imaging, 5 visual camera 6 . Therefore, data completion and denoising are of interest and have drawn much attention for many years 7–9 …”

Section: Introductionmentioning

confidence: 99%