Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

In this paper, we propose a novel robust algorithm for image recovery via affine
transformations and the L 2, 1 norm. To be robust against miscellaneous adverse effects …

In this work, a new robust regularized shrinkage regression method is proposed to recover
and align high‐dimensional images via affine transformation and Tikhonov regularization …

In this paper, we propose a novel robust algorithm for image recovery via affine
transformations, the weighted nuclear, L∗, w, and the L2, 1 norms. The new method …

Robust and efficient image alignment remains a challenging task, due to the massiveness of
images, great illumination variations between images, partial occlusion, and corruption. To …

Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision
because it is an essential function in many practical applications. In this paper, we propose a …

In this study, an effective robust PCA is developed for joint image alignment and recovery via
L2, 1 norms and affine transformations. To alleviate the potential impacts of outliers, heavy …

The presence of pathologies in magnetic resonance (MR) brain images causes challenges
in various image analysis areas, such as registration, atlas construction and atlas-based …

We present a common framework for the simultaneous segmentation and recovery of
pathological magnetic resonance (MR) brain images, where low-rank and sparse …

Point set registration (PSR) is a crucial problem in computer vision and pattern recognition.
Existing PSR methods cannot align point sets robustly due to degradations, such as …