Image Completion with Filtered Low-Rank Tensor Train Approximations

Zdunek, Rafał; Fonał, Krzysztof; Sadowski, Tomasz

doi:10.1007/978-3-030-20518-8_20

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to further improve the performance, we perform the mode permutation [ 19 ] after sampling. Here we give an example of the mode permutation as shown in Figure 3 .…”

Section: Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion

Liu

Tang

2023

Sensors

View full text Add to dashboard Cite

Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does not have a strong low-rank property. In order to enhance the low-rank property, we propose a novel method called sub-image based low-rank tensor completion (SLRTC) for image restoration. We first sample a color image to obtain sub-images, and adopt these sub-images instead of the original single image to form a tensor. Then we conduct the mode permutation on this tensor. Next, we exploit the tensor nuclear norm defined based on the tensor-singular value decomposition (t-SVD) to build the low-rank completion model. Finally, we perform the tensor-singular value thresholding (t-SVT) based the standard alternating direction method of multipliers (ADMM) algorithm to solve the aforementioned model. Experimental results have shown that compared with the state-of-the-art tensor completion techniques, the proposed method can provide superior results in terms of objective and subjective assessment.

show abstract

“…In order to further improve the performance, we perform the mode permutation [ 19 ] after sampling. Here we give an example of the mode permutation as shown in Figure 3 .…”

Section: Proposed Modelmentioning

confidence: 99%

“…The mode permutation option can avoid scanning an entire image, which reduces the overall computational complexity [ 19 ].…”

Section: Proposed Modelmentioning

confidence: 99%

Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion

Liu

Tang

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…We compared the proposed methods with the following: FAN (filtering by adaptive normalization) and EFAN (efficient filtering by adaptive normalization) [60], SmPC-QV (smooth PARAFAC tensor completion with quadratic variation) [38], LRTV (low-rank total-variation) [61], TMAC-inc (low-rank tensor completion by parallel matrix factorization with the rank-increasing) [62], C-SALSA (constrained split augmented Lagrangian shrinkage algorithm) [63], fALS (filtered alternating least-squares) [64], and KA-TT (ket augmentation tensor train) [65]. FAN and EFAN are based on adaptive Gaussian low-pass filtration.…”

Section: Setupmentioning

confidence: 99%

Image Completion with Hybrid Interpolation in Tensor Representation

Zdunek¹,

Sadowski²

2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The issue of image completion has been developed considerably over the last two decades, and many computational strategies have been proposed to fill-in missing regions in an incomplete image. When the incomplete image contains many small-sized irregular missing areas, a good alternative seems to be the matrix or tensor decomposition algorithms that yield low-rank approximations. However, this approach uses heuristic rank adaptation techniques, especially for images with many details. To tackle the obstacles of low-rank completion methods, we propose to model the incomplete images with overlapping blocks of Tucker decomposition representations where the factor matrices are determined by a hybrid version of the Gaussian radial basis function and polynomial interpolation. The experiments, carried out for various image completion and resolution up-scaling problems, demonstrate that our approach considerably outperforms the baseline and state-of-the-art low-rank completion methods.

show abstract

Image Completion with Filtered Alternating Least Squares Tucker Decomposition

Sadowski

Zdunek

2019

2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)

View full text Add to dashboard Cite

Image Completion with Filtered Low-Rank Tensor Train Approximations

Cited by 4 publications

References 20 publications

Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion

Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion

Image Completion with Hybrid Interpolation in Tensor Representation

Image Completion with Filtered Alternating Least Squares Tucker Decomposition

Contact Info

Product

Resources

About