Quad-DIP for X-ray cargo image decomposition

To identify different cargoes on vehicles accurately in scanned image is a tough issue. An unsupervised image de-composition method, based on a novel dual-stage double-DIP (DDIP) network, named as Quad-DIP, was proposed for the decomposition of X-ray scanned image of a cargo vehicle into vehicle and goods separately without ground truth data. The model could be effectively trained based on the fact that, firstly, the structure contents of same type vehicles were similar in the images, and secondly, the contents of goods on different vehicles were different and independent to each other. Our work focus on the content-wise correlation between them. The vehicle structure could be identified from two inputs containing the same type of vehicles, and the image could be decomposed into two components of vehicle structure and cargo information accurately after the training of Quad-DIP. We examine the accuracy of this method on the collected X-ray cargo vehicle dataset. The decomposition of Quad-DIP was more accurate than those of other published methods in literature.