Super-Resolution Network for X-Ray Security Inspection

X-ray imaging is widely used in airports and transportation for security maintaining. Conventional x-ray images often suffer from noise interference, over sharpening or detail loss, especially in areas where multiple objects overlap each other. To overcome the shortcomings of traditional methods, this article presents a method to reveal the details based on convolutional neural network (CNN). We put forward a well-designed super resolution (SR) network exploiting selfguided architecture to fuse multi-scale information. At each scale, we adopt residual feature aggregation strategy for extracting representative details. We also find it is beneficial to establish links between high energy (HE) and low energy (LE) images, thus the restored images show more fine textures and better material resolution. The comparison experiments demonstrate that the proposed network outperforms traditional approaches for restoring details and suppressing noise effectively.