Bayesian framework for solving transform invariant low-rank textures

Comparing to the low level local features, Transform Invariant Low-Rank Textures (TILT) can in some sense globally rectify a large class of low-rank textures in 2D images, and thus more accurate and robust. However, TILT is still rather rudimentary, and have some limitations in applications. In this paper, we proposed a novel algorithm for better solving TILT. Our method is based on the application of Bayesian framework in robust principal component analysis (RPCA), besides less local minima, nonparametric Bayesian method introduces the uncertainty in the parameters, which make our new algorithm can handle more complex situations. Experimental results on both synthetic and real data indicate that our new algorithm outperforms the existing algorithm especially for the case with corruptions and occlusions.