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In this paper, a novel energy functional minimization model is proposed for ultrasound images denoising. A controllable regularized term and the variational method are employed in the process of speckle noise. This model not only improves the plasticity of the model, but also improves the effect and efficiency of noise removal. The new model has different diffusion performance in different regions. At the same time, the diffusion performance is related to the parameters introduced by the proposed model. Numerical simulation results show that different parameters have different denoising effects, and the proposed model for speckle noise removal is superior to traditional models.

Active contour models have been extensively applied to image processing and computer vision. In this paper, we present a novel adaptive method combines the advantages of the SBGFRLS model and GAC model. It can segment images in presence of low contrast, noise, weak edge and intensity inhomogeneity. Firstly, a region term is introduced. It can be seen as the global information part of our model and it is available for images with low gray values. Secondly, Legendre polynomials are employed in the local statistical information part to approximate region intensity and then our model can deal with images with intensity inhomogeneity or weak edges. Thirdly, a correction term is selected to improve the performance of curve evolution. Synthetic and real images are tested and Dice similarity coefficients of different models are compared in this paper. Experiments show that our model can obtain better segmental results.

In this paper, a static electromagnetic field problem will be solved. The method to solve the problem is Finite Difference Method ( FDM ). The software MATLAB is the main environment used to finish the numerical computing. Finally, we found using Finite Difference Method to solve a static electromagnetic field problem is quite convenient. We also can draw the two-dimension and three-dimension graph of the distribution of the electromagnetic field. Furthermore, we discuss the advantages of using FDM to solve the problem, and the disadvantages still exist in this method.