Cong Peng

TL;DR: A generative adversarial network (GAN) based x-ray image denoising method that generates more plausible-looking images, which contains more details, compared with the traditional convolutional neural network (CNN) based method.

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 paper, a ray irradiation device consisting of a ray source, a shielding shell and a travel mechanism is described, where the ray source is used for providing rays, and the shielding shell is provided with a cavity which is used to contain the ray and an opening which is communicated with the cavity, so that the rays can radiate outwards through the opening.