activity
20132023
most citedAn Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum

141 citations · 168 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution

Hansheng Guo, Juncheng Li, Guangwei Gao +2

Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous m…

cs.CV2017★ 14 cited

Video Denoising and Enhancement via Dynamic Video Layering

Han Guo, Namrata Vaswani

Video denoising refers to the problem of removing "noise" from a video sequence. Here the term "noise" is used in a broad sense to refer to any corruption or outlier or interferenc…

cs.LG2016

Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

Namrata Vaswani, Han Guo

Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate soluti…

cs.LG2016★ 7 cited

Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

Namrata Vaswani, Han Guo

Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate soluti…

cs.IT2016★ 5 cited

Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees

Jinchun Zhan, Brian Lois, Namrata Vaswani

In this work, we study the online robust principal components' analysis (RPCA) problem. In recent work, RPCA has been defined as a problem of separating a low-rank matrix (true dat…

cs.IT2013★ 141 cited

An Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum

Han Guo, Chenlu Qiu, Namrata Vaswani

This paper designs and evaluates a practical algorithm, called practical recursive projected compressive sensing (Prac-ReProCS), for recovering a time sequence of sparse vectors $S…