  In this article, we address the issue of recovering latent transparent layers
from superimposition images. Here, we assume we have the estimated
transformations and extracted gradients of latent layers. To rapidly recover
high-quality image layers, we propose an Efficient Superimposition Recovering
Algorithm (ESRA) by extending the framework of accelerated gradient method. In
addition, a key building block (in each iteration) in our proposed method is
the proximal operator calculating. Here we propose to employ a dual approach
and present our Parallel Algorithm with Constrained Total Variation (PACTV)
method. Our recovering method not only reconstructs high-quality layers without
color-bias problem, but also theoretically guarantees good convergence
performance.
