Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

References

• Abhishek K, Pratihar V, Shandilya SK, et al. An intelligent approach for mining knowledge graphs of online news. Int J Comput Appl. 2022;44(9):838–846.
   Google Scholar
• Wang Z, Liu M, Dong M, et al. Riemannian alternative matrix completion for image-based flame recognition. IEEE Trans Circuits Syst Video Technol. 2016;27(11):2490–2503.
   Web of Science ®Google Scholar
• Jallouli M, Lajmi S, Amous I. When contextual information meets recommender systems: extended svd++ models. Int J Comput Appl. 2022;44(4):349–356.
   Google Scholar
• Chen Z, Xu Z, Wang D. Deep transfer tensor decomposition with orthogonal constraint for recommender systems. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2021; Vol. 35. p. 4010–4018.
   Google Scholar
• Tao X, Wang Y, Lin L, et al. Learning to reconstruct ct images from the vvbp-tensor. IEEE Trans Med Imaging. 2021;40(11):3030–3041.
   PubMed Web of Science ®Google Scholar
• Xu H, Jiang J, Feng Y, et al. Tensor completion via hybrid shallow-and-deep priors. Appl Intell. 2022. DOI:10.1007/s10489-022-04331-4.
   Web of Science ®Google Scholar
• Xue J, Zhao Y, Liao W, et al. Enhanced sparsity prior model for low-rank tensor completion. IEEE Trans Neural Netw Learn Syst. 2019;31(11):4567–4581.
   Web of Science ®Google Scholar
• Xue J, Zhao Y, Liao W, et al. Nonconvex tensor rank minimization and its applications to tensor recovery. Inf Sci (NY). 2019;503:109–128.
   Web of Science ®Google Scholar
• Zheng J, Qin M, Xu H, et al. Tensor completion using patch-wise high order hankelization and randomized tensor ring initialization. Eng Appl Artif Intell. 2021;106:104472.
   Google Scholar
• Jiang J, Feng Y, Xu H, et al. Hyperspectral and multispectral data fusion via joint local-nonlocal modeling and truncation operator. IEEE J Sel Top Appl Earth Obs Remote Sens. 2022;15:5880–5893.
   Web of Science ®Google Scholar
• Zhang M, Desrosiers C. High-quality image restoration using low-rank patch regularization and global structure sparsity. IEEE Trans Image Process. 2018;28(2):868–879.
   Web of Science ®Google Scholar
• Feng Y, Xu H, Jiang J, et al. Icif-net: intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection. IEEE Trans Geosci Remote Sens. 2022;60:1–13.
   Google Scholar
• Gu Y, Xu H, Quan Y, et al. Orsi salient object detection via bidimensional attention and full-stage semantic guidance. IEEE Trans Geosci Remote Sens. 2023;61:1–13.
   Web of Science ®Google Scholar
• Yan S, Zhang X. Pcnet: partial convolution attention mechanism for image inpainting. Int J Comput Appl. 2022;44(8):738–745.
   Google Scholar
• Xu H, Qin M, Chen S, et al. Hyperspectral–multispectral image fusion via tensor ring and subspace decompositions. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:8823–8837.
   Web of Science ®Google Scholar
• Xu H, Qin M, Yan Y, et al. Nonlocal b-spline representation of tensor decomposition for hyperspectral image inpainting. Signal Process. 2023;206:108888.
   Google Scholar
• Madadi Y, Seydi V, Hosseini R. Multi-source domain adaptation-based low-rank representation and correlation alignment. Int J Comput Appl. 2022;44(7):670–677.
   Google Scholar
• Zheng J, Yang P, Yang X, et al. Truncated low-rank and total p variation constrained color image completion and its moreau approximation algorithm. IEEE Trans Image Process. 2020;29:7861–7874.
   Web of Science ®Google Scholar
• Wu P-L, Zhao X-L, Ding M, et al. Tensor ring decomposition-based model with interpretable gradient factors regularization for tensor completion. Knowl Based Syst. 2023;259:110094.
   Google Scholar
• Zheng J, Lou K, Yang X, et al. Weighted mixed-norm regularized regression for robust face identification. IEEE Trans Neural Netw Learn Syst. 2019;30(12):3788–3802.
   PubMed Web of Science ®Google Scholar
• Zha Z, Wen B, Yuan X, et al. Low-rankness guided group sparse representation for image restoration. IEEE Trans Neural Netw Learn Syst. 2022;1–15. DOI:10.1109/TNNLS.2022.3144630.
   Web of Science ®Google Scholar
• Wang Z-Y, Li XP, So HC. Robust matrix completion based on factorization and truncated-quadratic loss function. IEEE Trans Circuits Syst Video Technol. 2023;33(4):1521–1534.
   Web of Science ®Google Scholar
• Zheng J, Jiang J, Xu H, et al. Manifold-based nonlocal second-order regularization for hyperspectral image inpainting. IEEE J Sel Top Appl Earth Obs Remote Sens. 2020;14:224–236.
   Web of Science ®Google Scholar
• Kanatsoulis CI, Fu X, Sidiropoulos ND, et al. Hyperspectral super-resolution: A coupled tensor factorization approach. IEEE Trans Signal Process. 2018;66(24):6503–6517.
   Web of Science ®Google Scholar
• Xue J, Zhao Y, Liao W, et al. Enhanced sparsity prior model for low-rank tensor completion. IEEE Trans Neural Netw Learn Syst. 2019;31(11):4567–4581.
   Web of Science ®Google Scholar
• Liu Y, Long Z, Huang H, et al. Low cp rank and tucker rank tensor completion for estimating missing components in image data. IEEE Trans Circuits Syst Video Technol. 2019;30(4):944–954.
   Web of Science ®Google Scholar
• Du S, Xiao Q, Shi Y, et al. Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion. Neurocomputing. 2021;458:204–218.
   Web of Science ®Google Scholar
• Du S, Liu B, Shan G, et al. Enhanced tensor low-rank representation for clustering and denoising. Knowl Based Syst. 2022;243:108468.
   Google Scholar
• Xu H, Zheng J, Yao X, et al. Fast tensor nuclear norm for structured low-rank visual inpainting. IEEE Trans Circuits Syst Video Technol. 2021;32(2):538–552.
   Web of Science ®Google Scholar
• Yokota T, Erem B, Guler S, et al. Missing slice recovery for tensors using a low-rank model in embedded space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018; p. 8251–8259.
   Google Scholar
• Yamamoto R, Hontani H, Imakura A, et al. Fast algorithm for low-rank tensor completion in delay-embedded space, In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022; p. 2048–2056.
   Google Scholar
• Li L, Jiang F, Shen R. Total variation regularized reweighted low-rank tensor completion for color image inpainting. In: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE; 2018; p. 2152–2156.
   Google Scholar
• Jiang F, Liu X-Y, Lu H, et al. Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE; 2018; p. 1363–1367.
   Google Scholar
• Zheng Y-B, Huang T-Z, Zhao X-L, et al. Tensor completion via fully-connected tensor network decomposition with regularized factors. J Sci Comput. 2022;92(1):8.
   Web of Science ®Google Scholar
• Condat L. Discrete total variation: new definition and minimization. SIAM J Imaging Sci. 2017;10(3):1258–1290.
   Web of Science ®Google Scholar
• Kolda TG, Bader BW. Tensor decompositions and applications. SIAM Review. 2009;51(3):455–500.
   Web of Science ®Google Scholar
• Liu J, Musialski P, Wonka P, et al. Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell. 2012;35(1):208–220.
   Web of Science ®Google Scholar
• Han X, Wu J, Wang L, et al. Linear total variation approximate regularized nuclear norm optimization for matrix completion. In: Abstract and Applied Analysis; Hindawi; 2014; Vol. 2014.
   Google Scholar
• Yokota T, Zhao Q, Cichocki A. Smooth parafac decomposition for tensor completion. IEEE Trans Signal Process. 2016;64(20):5423–5436.
   Web of Science ®Google Scholar
• Chen Y-L, Hsu C-T, Liao H-YM. Simultaneous tensor decomposition and completion using factor priors. IEEE Trans Pattern Anal Mach Intell. 2013;36(3):577–591.
   Web of Science ®Google Scholar
• Li X, Ye Y, Xu X. Low-rank tensor completion with total variation for visual data inpainting. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2017; Vol. 31.
   Google Scholar