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BackgroundThe quality of neutron radiographic images is mainly evaluated by human visual system (HVS), but HVS cannot be used as a real-time auxiliary for optimization parameters of neutron imaging systems.PurposeThis study aims to evaluate the quality of neutron radiographic images by no-reference image quality assessment (NR-IQA) and provide an effective approach for the parameters optimization of neutron imaging systems.MethodsFirstly, the plain natural images distorted with different distortion levels and types were labelled with quality scores to construct an experimental dataset by the gradient magnitude similarity deviation (GMSD) method. Then, the residual network (ResNet) model was employed to evaluate the quality of neutron radiographic images without reference images. Finally, the goal of extracting features and assessing the quality of neutron radiographic images was achieved by training the ResNet.ResultsThe model performs well in the test set of the experimental dataset and the quality prediction of the two group authentic neutron radiographic images.ConclusionsThe proposed quality assessment method could be used for the quality prediction of neutron radiographic images.

• A neutron image denoising method based on multi-branch network is proposed. • Each branch is separately targeted for denoising one type of noise. • It would be more effective to use a combination of pixel-level and feature-level fusion to synthesise the noise residuals. • Multi-branch network structure is more flexible and effective in mixed noise denoising compared to a single model. Neutron imaging technology is a non-destructive testing method that uses the interaction characteristics between neutrons and matter to obtain images. In the process of neutron imaging, it will be affected by many noises. To address Gaussian, Poisson and Gamma white spot noise in neutron images, a new denoising method based on multi-branch network is proposed. The basic idea is to use different network branches to deal with different types of noise, resulting in more accurate and effective denoising. Finally, the weight fusion module network was used to synthesize the noise residual output of each branch to generate the final total residual image. Experimental results show that the proposed method is significantly more effective in dealing with mixed noise in neutron images compared to several other single-model networks, proving the superiority of the multi-branch network in the field of neutron image denoising. [ABSTRACT FROM AUTHOR]

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]

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]

Neutron imaging (NI) has been widely employed in non-destructive investigations. Since the image quality assessment (IQA) method can be beneficial in reflecting the performance of imaging systems and image processing algorithms, we propose a proof-of-concept IQA method for the NI system based on a deep bilinear convolutional neural network (CNN) framework with two designed datasets. Due to the lack of neutron IQA database, different levels of authentic distortion induced by NI are first simulated on the natural and neutron radiographic images to generate the pre-training and fine-tuning datasets, respectively. Then, the gradient magnitude similarity deviation (GMSD) algorithm and transfer learning method are respectively employed to label the above datasets and optimize the prediction performance. Experimental results demonstrate that the proposed method can maintain good consistency with human perception in predicting the quality scores of both the authentic and enhanced neutron radiographic images. [ABSTRACT FROM AUTHOR]