Guo Xiaojing

TL;DR: In this paper, a safety inspection system consisting of a first radiation emission device, a radiation receiving device, and one or more inspection devices including a moving frame is presented, where the moving frame moves in a direction to move the first radiation emitting device and the radiation receiving devices to pass an inspection area of the target.

TL;DR: Zhang et al. as mentioned in this paper proposed a single-image super-resolution reconstruction method based on a deep residual network, which mainly comprises a first step of performing block extraction and pixel averaging processing on an image in a sample image database to obtain a corresponding high-resolution and low-resolution training image sets; a second step of constructing a deep convolutional neutral network with a residual structure for iterative training, and then inputting the training set obtained in the first step to the neural network constructed in the second step, according to a data model obtained by training, realizing the

TL;DR: Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise.

TL;DR: In this article, the authors proposed a vehicle radiation imaging detection system consisting of a rack, wherein a radiation source, a front collimator and an array detector are arranged on the rack, and a detection channel is formed between the front collimer and the array detector; a ray is collimated into a fan-shaped ray beam with a small penumbra region by virtue of the frontcollimator with a large collimation ratio.

TL;DR: In this article, a ray source, a front collimator, and an array detector are used to reduce the detecting dose of a radiation imaging system, where a detecting channel for passing through a to-be-detected object is formed between the frontcollimator and the array detector.