This paper studies the problem of simultaneously aligning a batch of linearly correlated
images despite gross corruption (such as occlusion). Our method seeks an optimal set of …

We propose a new provable method for robust PCA, where the task is to recover a low-rank
matrix, which is corrupted with sparse perturbations. Our method consists of simple …

In this paper, we propose a new tool to efficiently extract a class of “low-rank textures” in a
3D scene from user-specified windows in 2D images despite significant corruptions and …

摘要综述了压缩传感, 矩阵秩最小化和低秩矩阵恢复等方面的基础理论及典型应用.
基于凸优化的压缩传感及由其衍生的矩阵秩最小化和低秩矩阵恢复是近年来的研究热点 …

This paper reviews the basic theory and typical applications of compressed sensing, matrix
rank minimization, and low-rank matrix recovery. Compressed sensing based on convex …

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate
its application for robust image restoration. In the literature, principal component pursuit …

We study robust PCA for the fully observed setting, which is about separating a low rank
matrix L and a sparse matrix S from their sum D= L+ S. In this paper, a new algorithm …

Transform invariant low-rank textures (TILT) is a novel and powerful tool that can effectively
rectify a rich class of low-rank textures in 3D scenes from 2D images despite significant …

In this paper, we present an attribute grammar for solving two coupled tasks: i) parsing a 2D
image into semantic regions; and ii) recovering the 3D scene structures of all regions. The …

In this paper, we present an attributed grammar for parsing man-made outdoor scenes into
semantic surfaces, and recovering its 3D model simultaneously. The grammar takes …