ArticleJournal of X-ray science and technology2024
Optimizing dual energy X-ray image enhancement using a novel hybrid fusion method.
Article in Journal of X-ray science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Enhanced image encryption with deep generative models using a self-attention mechanism.Scientific reports · 2026Article
- Identification of camouflage military individuals with deep learning approaches DFAN and SINETV2.Scientific reports · 2025Article
- Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAirport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.
objectiveTo address the challenge of noise interference in DEXI, this study aims to develop and validate a robust denoising technique using the Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT).
methodsThe proposed method targets and removes background and Poisson noise in DEXI images, improving object recognition accuracy. During the denoising process, images are decomposed into several subbands, and thresholding techniques are applied to minimize noise while preserving important information. The images are then reconstructed to provide a cleaner and more accurate depiction of scanned objects.
resultsExperimental results demonstrate the effectiveness of the DWT and SWT-based denoising strategy in preserving critical data while suppressing noise in DEXI. The performance of the denoising technique is quantified using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The proposed system achieved an average PSNR of 35.23 and an MSE of 19.52 for 256×256 DEXI images, and an average PSNR of 36.01 and an MSE of 16.29 for 512×512 DEXI images.
conclusionThe results highlight the achievement of the proposed approach in enhancing the quality of DEXI for improved security screening, demonstrating its potential application in airport security systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.