Efficient blind separation of reflection layers with nonparametric transformations
Superimposed images are very common when taking photos behind glass. We address the reflection separation problem using multiple superimposed images photographed in different viewpoints. With viewpoints changing, the reflected scenes could contain arbitrarily complicated variations between mixtures, like human's motions or other nonrigid motions. In this article, we propose a moderate hypothesis to tackle the reflected scenes' arbitrary variations as well as the parametric transformations of transmitted scenes. To rapidly recover high-quality image layers, we propose an Efficient Superimposition Recovering Algorithm (ESRA) by extending the framework of accelerated gradient method. Our recovering method has good converging performance and is more than 30 times faster than state-of-the-art methods. Experimental results on synthetic and real world images demonstrate that our method is promising.