Transparent layer separation for dual energy imaging

Dual energy X-ray imaging can separate materials of different properties into different layers for more detailed inspection. However, motion during the acquisition of dual energy images causes significant challenges to layer separation. We propose a novel separation algorithm based on the sparse features in gradient domain. Instead of decomposing the image intensity values, we separate sparse gradient features to different layers and then reconstruct the layers based on the decomposed gradient fields by solving the Poisson Equation. The structure tensor, based on directional quadrature filters, is used to classify and separate the gradient field. Evaluation on both synthesized data and real datasets proves its effectiveness and robustness to complex non-rigid motion.