Image alignment refers to the problem of transforming different images into the same coordinate system. Peng et al. proposed to solve the problem by rank minimization based on the assumption that a batch of aligned images should form a low-rank matrix. The sparse component E models local differences among images.
Publication
Y. Peng, A. Ganesh, J. Wright, W. Xu, Y. Ma, "RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 34, Issue 11,pages 2233-2246, 2012.
Y. Li, V. Monga, “SIASM: Sparsity-Based Image Alignment and Stitching Method for Robust Image Mosaicking”, IEEE International Conference on Image Processing, ICIP 2016, 2016.
Y. Wu, B. Shen, H. Ling, "Online robust image alignment via iterative convex optimization", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 20112, pages 1808–1814, 2012.
W. Song, J. Zhu, Y. Li, C. Chen, " Image alignment by online robust PCA via stochastic gradient descent", IEEE Transactions on Circuits and Systems for Video Technology, Volume 26, No. 7, pages 1241–1250, 2016.
Q. Zheng, Y. Wang, P. Heng, “Online Robust Image Alignment via Subspace Learning from Gradient Orientations”, IEEE International Conference on Computer Vision, ICCV 2017, 2017.
J. Liu, D. He, X. Zeng, M. Wang, X. Xiu, W. Liu, W. Li, “ManiDec: Manifold Constrained Low-Rank and Sparse Decomposition”, IEEE Access 2019, 2019.
H. Likassa, W. Fang, Y. Chuang, “Modified robust image alignment by sparse and low rank decomposition for highly linearly correlated data", IEEE International Conference on Intelligent Green Building and Smart Grid, IGBSG 2018, pages 1–4, 2018.
H. Likassa, W. Fang, J. Leu, “Robust image recovery via affine transformation and l2,1 norm", IEEE Access, Volume 7, pages125011–125021, 2019.
H. Likassa, “New Robust Principal Component Analysis for Joint Image Alignment and Recovery via Affine Transformations and Frobenius and l2,1 Norms”, Hindawi Internartional Journal of Mathematics and Mathematical Sciences, 2020.
H. Likassa, "New Advanced Robust PCA via A Sets of Affine Transformations and l2,1 Norms for Low Rank, Sparse and Gaussian Decomposition", Hindawi Internartional Journal of Mathematics and Mathematical Sciences, 2021.