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Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well. • Proposed a deep learning-based method to remove hybrid noise and blur from neutron images. • A fusion block combines a dense residual block and a spatial channel attention mechanism. • Perceptual loss is used to make our network efficiently learn the high-level information of the image. • Visual evaluation and quantitative analysis illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A new structure of the Penning ion gauge (PIG) ion source used for neutron tube is designed, and the experiments on beam current extraction of the PIG negative hydrogen (H −) ion source are performed. The influences of the magnetic field of the ion source, different cathode materials and the aperture size of the extraction cathode on the negative hydrogen ion flow are studied. The neutron experiments are performed with a PIG negative hydrogen (H − PIG) ion source D–D neutron tube, and the experimental data demonstrate that this sort of negative hydrogen ion source can be used to produce neutron tubes with good performance. Highlights • A new structure of PIG ion source used for neutron tube is proposed. • Beam current extraction experiments of PIG H − ion source are performed. • The influence of several factors on negative hydrogen ions flow are studied. • The neutron experiments are performed with a PIG H − ion source D–D neutron tube. • This kind H − ion source can be used to produce neutron tubes with good performance. [ABSTRACT FROM AUTHOR]

• A study on the use of deep learning model in the field of neutron image restoration is performed. • A lightweight, convenient and efficient deep learning network is proposed for neutron image restoration. • The X-ray dataset solves the problem of lack of real neutron image datasets. • The pre-training model achieves good results in real neutron image restoration. Traditional neutron image restoration (NIR) methods lack flexibility and generalization in dealing with multiple image restoration tasks, which indirectly limits the practical applications of neutron images. Deep convolutional neural networks (CNNs) have achieved impressive success in image restoration, so this paper preliminarily explores and applies deep CNNs in NIR. Considering the characteristics and applications of neutron images, we propose a fast and lightweight densely connected attention U-Net (DAUNet) for NIR. We strengthen the network's attention to the image structural information and the processing at different frequencies by adding an attention mechanism to the symmetric skip connection under each scale of U-Net. The proposed DAUNet uses a single model for recovering images with different degradations, such as additive noise and blur. Extensive experiments on synthetic and real neutron-degraded images are conducted, and the results show that DAUNet is effective and efficient in NIR and achieves satisfactory results in terms of both comprehensive performance (PSNR) and intrinsic metrics (parameters, memory cost and running time), which are highly attractive for practical applications. In addition, to address the problem of lacking real NIR datasets, we use X-ray images that the imaging principles are closer to that of neutron imaging as data drivers for the network. [ABSTRACT FROM AUTHOR]

As the traditional reactor and accelerator neutron sources are immovable, the research on the compact neutron radiography becomes more and more important. In this study, a portable neutron tube is employed for the compact thermal neutron radiography system. To realize ideal image quality, the optimal material selection and geometry design (viz. the diameter of the aperture, the shape of the channel and the lining material) of the proposed collimator are thereafter carried out by the SuperMC software. Simulation results show that the normalized thermal neutron flux of 1. 42 × 1 0 − 6 ncm − 2 s − 1 can be successfully realized at the exit of the collimator by the proposed collimator with a satisfied nonuniformity of 4.6%. • The neutron tube is used as the source of the compact neutron radiography system. • The proposed collimator is simulated by the SuperMC software. • 45# transformer oil is used for heat dissipation and insulation. • Graphite and beryllium are selected as the reflector materials. • The shape of collimator is cylindrical-conical. [ABSTRACT FROM AUTHOR]

• A simple white spots removal method is proposed for degraded neutron images. • A'detection-location-removal' iterative strategy is introduced for the white spots detection and removal. • The qualitative and quantitative analysis are used for evaluating the performance of the mentioned methods. • The proposed method has a better performance in spots removal and details preservation than the comparison methods. Neutron radiography (NR) technology has been widely used in non-destructive investigations. However, the neutron images are always polluted by random white spots due to the interactions between the gamma rays, scattered neutron and other radiation and chip of the charge-coupled device (CCD) camera. In this paper, we propose a novel denoising method based on a 'detection-location-removal' iterative strategy, which adopts an improved robust principal component analysis (RPCA) in combination with the suitable threshold selection strategy and median filters for white spots noise removal of neutron images. Experimental results based on both simulation testing and real neutron images show that the proposed method is effective in improving neutron image quality in terms of numerical criteria and visual results. The comparison with the other denoising methods shows that the proposed method possesses higher robustness and effectiveness in practical denoising applications. [ABSTRACT FROM AUTHOR]

• A collaborative filtering method is adopted for gamma white spots detection. • The spots are removed through a"detection-corrosion" step by step strategy. • The proposed method can effectively remove the gamma white spots andsimultaneouslypreservethe details in the neutron images. In CCD-based neutron radiography and tomography, resulting digital neutron images are usually degraded by gamma white spots caused by the interaction between high energy gamma radiation and the charge-coupled device (CCD) detector. The gamma white spots have a major impact on the accuracy of quantitative data analysis and are difficult to remove due to their high magnitudes and non-uniform distribution. In this paper, we put forward a novel iterative filtering method based on a "detection-corrosion" strategy for degraded neutron image denoising. A collaborative filtering method is adopted via integrating a spatially adaptive filter and a median filter for gamma white spots detection and removal. The experimental results show that the proposed method can achieve satisfactory denoising effect with preferable preservation of image details. [ABSTRACT FROM AUTHOR]

Neutron radiography (NR) has been widely used in non-destructive investigations. Since the industrial neutron radiographic images (NRIs) usually suffer from unclear defect features, insufficient data volume, and low efficiency of manual detection, we propose a proof-of-concept defect-detection method based on the modified YOLO network for the degraded NRIs in this paper. Firstly, 6336 radiographic images including crack, inclusion, blow hole, and residual core are built as the defect-detection dataset. Secondly, the types and the relative coordinates of defects are labeled by a graphical image annotation tool (i.e., Labelimg). Finally, the adaptive spatial feature fusion (ASFF) and convolutional block attention module (CBAM) are introduced to the modified YOLO network to enhance its ability of small-size defect detection. Experimental results demonstrate that the proposed method can achieve the average accuracy of 98.1% and detection rate of 85.985 frames per second (FPS) with a single NVIDIA GeForce RTX 4090 GPU on the built radiographic image dataset. In addition, the proposed method shows a good potential in detecting the above defects contained in the real NRIs. • A YOLO network-based automatic defect detection method with higher accuracy and lower detection time is proposed. • Data augmentation is employed to build a large-scale radiographic-image dataset with 6336 defect images. • ASFF and CBAM are used to solve the inconsistency of different-scale features and enhance the small-size detection ability. [ABSTRACT FROM AUTHOR]

Objective image quality metric is a challenging problem for neutron radiography due to the lack of reference images. The existing no-reference image quality metric (NR-IQM) method designed for neutron radiographic images (NRIs) still exists two main shortcomings. Firstly, the limited L/D of the collimator would inevitably lead to geometric blur in the NRIs. However, the special distortion type of mixed Poisson–Gaussian noise is merely considered but ignores the blur distortion. Secondly, the distortion levels of Poisson noise added to an image cannot be adjusted. In order to address these two issues, a more practical objective NR-IQM method is proposed to predict the perceptual quality of NRIs in this paper. Experimental results demonstrate that the proposed method shows good consistency with the quality of authentic NRIs and also performs well in reflecting the tiny quality difference of enhanced NRIs, thus demonstrating its application potential in neutron radiography. [ABSTRACT FROM AUTHOR]