Automatic prohibited item detection in security inspection X-ray images is necessary for transportation. The abundance and diversity of the X-ray security images with prohibited item, termed as prohibited X-ray security images, are essential for training the detection model. In order to solve the data insufficiency, we propose a Region-Wise Style-Controlled Fusion (RWSC-Fusion) network, which superimposes the prohibited items onto the normal X-ray security images, to synthesize the prohibited X-ray security images. The proposed RWSC-Fusion innovates both network structure and loss functions to generate more realistic X-ray security images. Specifically, a RWSC-Fusion module is designed to enable the region-wise fusion by controlling the appearance of the overlapping region with novel modulation parameters. In addition, an Edge-Attention (EA) module is proposed to effectively improve the sharpness of the synthetic images. As for the unsupervised loss function, we propose the Luminance loss in Logarithmic form (LL) and Correlation loss of Saturation Difference (CSD), to optimize the fused X-ray security images in terms of luminance and saturation. We evaluate the authenticity and the training effect of the synthetic X-ray security images on private and public SIXray dataset. The results confirm that our synthetic images are reliable enough to augment the prohibited X-ray security images.

Abstract Existing Generative Adversarial Networks (GAN)‐based face hallucination algorithms are hard to control the face fidelity of the generated samples, and easily generate flawed faces with unfavourable artefacts and distortions. To address this problem, the authors propose a fidelity‐controllable face super‐resolution (FSR) network boundary equilibrium face super‐resolution generative adversarial networks (BESRGAN), a fidelity ratio is introduced in their network to control how much the adversarial effect the discriminator is put on the generator; therefore, the authors’ network better trades off the objective and perceptual quality. Additionally, the authors design an equilibrium perceptual discriminator to match the perception loss distributions. Under the equilibrium constraint, the discriminator pays more attention to learning fine‐grained feature statistics of ground truths, and further guides the generator to produce photo‐realistic faces, especially in terms of facial textures. Moreover, the authors propose a novel channel‐spatial attention module (CSAM) to eliminate local distortions, by further fusing richer information from the facial prior knowledge and global high‐level facial descriptions. Extensive experiments illustrate that the authors’ approach preserves high pixel‐wise accuracy while achieving superior visual performance against state‐of‐the‐art methods. Specifically, the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) of the authors’ proposed BESRGAN rise 0.64 dB and 0.02 for CelebA compared with one of the state‐of‐the‐art face super‐resolution (FSR) methods.

Measurements of subjective picture impairment as a function of network loading in a simulated ATM network are reported. The simulation indicated that cells tend to be discarded in bursts, the frequency and severity of which can be related to the loading by a threshold model. The effect of the discards on broadcast-style video, coded using a single-layer H.261-type method, was found to be a function of scene content and movement at the instant of occurrence. If the visibility of cell discards is maintained at or below threshold in worst-case scenes, the study indicated that network loadings around 55% for a multiplex of 16 video sources and around 70% for a multiplex of 48 video sources are achievable.