Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Wang, Ruixuan; Trucco, Emanuele

doi:10.1109/iccv.2013.137

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Here, the low rank matrix Z can be seen as a rough similarity matrix, and the final partitioning result can be obtained by conducting spectral clustering with a refined similarity matrix. Face Analysis [59,[71][72][73] X * ADMM Person Re-Identification [74] X * others Visual Tracking [19,75] X * others 3D Reconstruction [20][21][22][23] X * ADMM Image denoising [30,[76][77][78] k i=1 wiσi ADMM Structure Recovery [79] r i=1 wiσi others Video Desnowing and Deraining [80] X * AM Salient Object Detection [24][25][26][27] X * ADMM Face Recognition [81][82][83] X * ADMM High Dynamic Range Imaging [53,84] X * , r i=k+1 σi ADMM Head Pose Estimation [85] X * ADMM Moving Object Detection [86] X * ADMM Reflection Removal [87] X * others Zero-Shot Learning [88] k i=r+1 σi others Speckle removal [89] k i=r+1 wiσi ADMM Image Completion [90] [93][94][95][96] X| * ADMM Image Restoration [97] X| * ADMM Image Classification [98][99][100][101][102][103] X * ADMM AAM fitting…”

Section: Subspace Clusteringmentioning

confidence: 99%

Low Rank Regularization: A Review

Nie

Wang

et al. 2018

Preprint

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Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications. Nevertheless, the intersection between them is very slight. In order to construct a bridge between practical applications and theoretical research, in this paper we provide a comprehensive survey for low rank regularization. We first review several representative machine learning models using low rank regularization, and then show their (or their variants) applications in solving practical issues, such as nonrigid structure from motion and image denoising. Subsequently, we summarize the regularizers and optimization methods that achieve great success in traditional machine learning tasks but are rarely seen in solving practical issues. Finally, we provide a discussion and comparison for some representative regularizers including convex and non-convex relaxations. Extensive experimental results demonstrate that non-convex regularizers can provide a large advantage over the nuclear norm, a convex regularizer that is widely used in solving practical issues.

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