Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.
In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.
Although the doctor-patient relationship (DPR) is a predictor of organizational citizenship behavior (OCB), the relations between DPR and OCB is not well understood. This study was designed to investigate the specific dimensions of OCB among physicians in China and determine the effects of DPR on OCB. The subjects were physicians at five large general hospitals in China, who were selected by random sampling methodology. Then, using empirical study methods, factor analysis and regression analysis for data collecting from 958 samples were made. Finally, we find that OCB consists of four dimensions including civic virtue, sportsmanship, altruism, and conscientiousness. DPR has significant negative impact on the civic virtue, sportsmanship, altruism, and conscientiousness behaviors, respectively. This finding suggests that effective strategies should be applied to promote physicians' OCB by improving DPR, which can enhance their job performances and improve the quality of health care delivery.
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