Linearized Alternating Direction Method with Adaptive Penalty and Warm Starts for Fast Solving Transform Invariant Low-Rank Textures

05/24/2012
by   Xiang Ren, et al.
0

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 deformation and corruption. The existing algorithm for solving TILT is based on the alternating direction method (ADM). It suffers from high computational cost and is not theoretically guaranteed to converge to a correct solution. In this paper, we propose a novel algorithm to speed up solving TILT, with guaranteed convergence. Our method is based on the recently proposed linearized alternating direction method with adaptive penalty (LADMAP). To further reduce computation, warm starts are also introduced to initialize the variables better and cut the cost on singular value decomposition. Extensive experimental results on both synthetic and real data demonstrate that this new algorithm works much more efficiently and robustly than the existing algorithm. It could be at least five times faster than the previous method.

READ FULL TEXT

page 2

page 10

page 12

research
12/15/2010

TILT: Transform Invariant Low-rank Textures

In this paper, we show how to efficiently and effectively extract a clas...
research
09/07/2018

Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

In tensor completion tasks, the traditional low-rank tensor decompositio...
research
02/08/2019

A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition

Recently, there has been a lot of research into tensor singular value de...
research
05/14/2020

Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence

In recent studies, the tensor ring (TR) rank has shown high effectivenes...
research
08/22/2023

Lifting Sylvester equations: singular value decay for non-normal coefficients

We aim to find conditions on two Hilbert space operators A and B under w...
research
07/01/2022

A Local Macroscopic Conservative (LoMaC) low rank tensor method for the Vlasov dynamics

In this paper, we propose a novel Local Macroscopic Conservative (LoMaC)...

Please sign up or login with your details

Forgot password? Click here to reset