In recent years, energy consumption in Qinhuangdao has continued to increase, especially during the heating period in winter, which has caused frequent occurrence of haze weather. Based on the air pollutant emission inventory of Qinhuangdao in 2019, this study simulated the concentration of primary pollutants SO2, NO2 and PM10 through WRF/CALPUFF model, and analyzed the contribution of seven major industries to pollutants under haze weather. The manufacturing industry had the greatest influence on SO2, NO2 and PM10 concentration, with contribution rates of 11.9%, 15.5% and 7.7%, respectively, the second was the energy supply industry, and its contribution rates were 5.11%, 7.68% and 5.16%, respectively, the shipping emission had the least influence SO2, NO2 and PM10 concentration, with contribution rates of 1.42%, 1.56% and 0.98%, respectively.
Image matching plays a very important role in the field of medical image, while the two image registration methods based on the mutual information and the optical flow are very effective. The experimental results show that the two methods have their prominent advantages. The method based on mutual information is good for the overall displacement, while the method based on optical flow is very sensitive to small deformation. In the breast DCE-MRI images studied in this paper, there is not only overall deformation caused by the patient, but also non rigid small deformation caused by respiratory deformation. In view of the above situation, the single-image registration algorithms cannot meet the actual needs of complex situations. After a comprehensive analysis to the advantages and disadvantages of these two methods, this paper proposes a registration algorithm of combining mutual information with optical flow field, and applies subtraction images of the reference image and the floating image as the main criterion to evaluate the registration effect, at the same time, applies the mutual information between image sequence values as auxiliary criterion. With the test of the example, this algorithm has obtained a better accuracy and reliability in breast DCE-MRI image sequences.
The restoration of blurred images corrupted by Poisson noise is an important task in various applications such as medical imaging, microscopy imaging, and so on. We focus on mean curvature-based regularization to address the Poisson noise image restoration problem. Furthermore, we derive a numerical algorithm based on the augmented Lagrange multiplier method with a splitting technique. In order to simultaneously demonstrate the effectiveness of the proposed method for Poisson noise removal with deblurring, we conduct systematic experiments on both nature images and biological images. Experimental results show that the proposed approach can produce higher quality results and more natural images compared to some state-of-the-art variational algorithms recently developed.
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