Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Contrast improvement of backscatter x-ray images by total variation method

Document Type : Research Paper

Authors

M. Azimi 1
E. Yahaghi 1
M. Roshan 2
E. Bayat 3
M. Mirzapour 4
A. Movafeghi 5
B. Rokrok 5

1 Department of Physics, Faculty of Basic Sciences, Imam Khomeini International University, P.O.BOX: 34148-96818, Qazvin - Iran

2 Nuclear Engineering School, Shahid Beheshti University, Postal code: 1983969411, Tehran-Iran

3 Radiation Applications Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box:11365-3486, Tehran-Iran

4 Department of Mathematics, Faculty of Basic Sciences, Bu-Ali Sina University, P.O.Box: 65178-38695, Hamedan - Iran

5 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran - Iran

https://doi.org/10.24200/nst.2023.1322
Abstract
The basis of backscatter X-ray imaging systems is the scattering of backscattered rays that have many applications in industry and inspection. In these images, organic materials with low atomic numbers, such as drugs and explosives, produce many scattered photons and are seen more clearly. These images are blurred due to the multiple scattered X-ray. Image processing methods can help improve contrast and material detection in backscatter X-ray (BX) images. In this research, the total variation method has been used to improve the contrast of the BX images. Firstly, the BX images of the various phantoms have been processed for evaluating the BX system, then many BX images of motorcycles are investigated. The results show that the total variation method increases the contrast of the backscattered X-ray images and can be used as the additional filter in the BX imaging system. Evaluation of experts shows that the contrast of the processed images has been significantly improved and increased between 20 to 40%.

Keywords

Backscatter x-ray imaging
Contrast improving
Inspection
Total variation
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