XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

Dual-energy X-ray security inspection imaging technology plays a crucial role in ensuring public safety, focusing on two tasks: tone mapping and pseudo-color imaging. This article introduces XTC-DDPM, a diffusion model tailored for dual-energy X-ray security inspection tone mapping of grayscale images and pseudo-color imaging. The objective is to enhance image quality and facilitate accurate item identification by security personnel. This article begins by constructing a dual-energy X-ray security inspection imaging dataset, named TCX-ray. Subsequently, our proposed method incorporates a multisource blur module based on the distribution of high-frequency information in the image. This module is applied to 16-bit X-ray security inspection raw images. During the multichannel forward diffusion process of the diffusion model, noise is gradually introduced to the multichannel target image until the image reaches a state of pure noise. The dual-energy 16-bit X-ray security inspection image, its blurred counterpart, and the noise image at each time period are combined. A dual-branch feature extraction denoising network is employed to predict the noise, followed by a reverse denoising process. This results in obtaining high- and low-energy 8-bit X-ray security inspection grayscale images, as well as pseudo-color images. Finally, the tone-mapped high- and low-energy X-ray images are fused to produce the grayscale output, while the pseudo-color imagery serves as an additional output. The proposed method is validated through both qualitative and quantitative experiments. The extensive experimental results demonstrate the superiority of our approach compared with the existing tone mapping and pseudo-color imaging methods for X-ray grayscale images.

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Publication Details

Journal
IEEE Sensors Journal
Published
2024-10-16
DOI
https://doi.org/10.1109/jsen.2024.3477927
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

Bowen Ma, Mingyuan Li, Tong Jia, Hao Wang et al.
IEEE Sensors Journal
Radiomics and Machine Learning in Medical Imaging
article

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

Bowen Ma, Mingyuan Li, Tong Jia, Hao Wang, Dongyue Chen, Da Cai
article en

Abstract

Dual-energy X-ray security inspection imaging technology plays a crucial role in ensuring public safety, focusing on two tasks: tone mapping and pseudo-color imaging. This article introduces XTC-DDPM, a diffusion model tailored for dual-energy X-ray security inspection tone mapping of grayscale images and pseudo-color imaging. The objective is to enhance image quality and facilitate accurate item identification by security personnel. This article begins by constructing a dual-energy X-ray security inspection imaging dataset, named TCX-ray. Subsequently, our proposed method incorporates a multisource blur module based on the distribution of high-frequency information in the image. This module is applied to 16-bit X-ray security inspection raw images. During the multichannel forward diffusion process of the diffusion model, noise is gradually introduced to the multichannel target image until the image reaches a state of pure noise. The dual-energy 16-bit X-ray security inspection image, its blurred counterpart, and the noise image at each time period are combined. A dual-branch feature extraction denoising network is employed to predict the noise, followed by a reverse denoising process. This results in obtaining high- and low-energy 8-bit X-ray security inspection grayscale images, as well as pseudo-color images. Finally, the tone-mapped high- and low-energy X-ray images are fused to produce the grayscale output, while the pseudo-color imagery serves as an additional output. The proposed method is validated through both qualitative and quantitative experiments. The extensive experimental results demonstrate the superiority of our approach compared with the existing tone mapping and pseudo-color imaging methods for X-ray grayscale images.

IEEE Sensors JournalVol. 24(23)
Northeastern University (CN)
National Natural Science Foundation of China, Higher Education Discipline Innovation Project, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 70%
Radiomics and Machine Learning in Medical Imaging
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