Low-Rank Modeling and Its Applications in Image Analysis
TLDR
This article reviews the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis, and summarizes the models and algorithms for low-Rank matrix recovery and illustrates their advantages and limitations with numerical experiments.Abstract:
Low-rank modeling generally refers to a class of methods that solves problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing, and bioinformatics. Recently, much progress has been made in theories, algorithms, and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this article with some discussions.read more
Citations
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Journal ArticleDOI
On the Applications of Robust PCA in Image and Video Processing
TL;DR: This paper presents the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing and provides perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.
Posted Content
Low-Rank Modeling and Its Applications in Image Analysis
TL;DR: Low-rank matrix recovery as discussed by the authors is a class of methods that solve problems by representing variables of interest as low-rank matrices, and it has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics.
Journal ArticleDOI
Low-Rank Quaternion Approximation for Color Image Processing
TL;DR: Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.
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Low CP Rank and Tucker Rank Tensor Completion for Estimating Missing Components in Image Data
TL;DR: This paper uses the alternating direction method of multipliers (ADMM) to reformulate the optimization model with two tensor ranks into its two sub-problems, and each has only one tensor rank optimization.
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