Industrial digital radiographic image denoising based on improved KBNet.

 Industrial digital radiography (DR) images are essential for industrial inspections, but they often suffer from strong scatter, cross-talk, electronic noise, and other factors that affect image quality. The presence of non-zero mean noise and neighborhood correlation loss in 1D array scanning poses significant challenges for denoising. To enhance the denoising process of industrial DR images and address the issues of low resolution and noise, we propose an improved KBNet (iKBNet) that incorporates lightweight modifications and introduces novel elements to the original KBNet.

Detection of prohibited and restricted object X-ray images based on Hi color space segmentation.

Background: X-ray imaging plays an important role in security inspection. However, the objects are complex, which makes it difficult to automatically detect prohibited and restricted objects.

Objective: This study aims to develop and test a detection method based on a new image segmentation scheme to solve the problem of detecting prohibited and restricted objects from pseudo-color X-ray images with complex backgrounds.

Physical design of photon-counting mode γ-ray large object radiation imaging system.

Background: The detectors of existing large object radiation imaging systems generally work under current-integration mode and cannot distinguish effective signals of unreacted photons from interfering signals of electronic noise and scattered photons, therefore, resulting in image quality deterioration.

Objective: This study aims to design a new photon-counting mode γ-ray large object radiation imaging system. Therefore, interfering signals with lower energy than effective signals can be eliminated by energy analysis.