Super-resolution (SR) reconstruction produces one or a set of high-resolution (HR) images from a set of low-resolution (LR) images. Regularization is a classical method for SR reconstruction. It contains only one fixed regularization parameter in most cases. Considering the difference between the LR images, such as noise, resolution, and the registration error, each LR image should correspond to different parameters according to a certain rule. Hence, we used generalized regularization schemes which contain multiple parameters. In order to obtain the optimal parameters, a new adaptive regularization method based on constrained particle swarm optimization algorithm (ARCPSO) is proposed. The initial value of each parameter is adaptive given. Furthermore, the particle swarm optimization (PSO) algorithm is applied to automatically select the optimal parameters in the proper range of initial values. The experimental results verify the effectiveness of our algorithm and demonstrate the superiority of our approach compared with traditional regularization methods.
In this paper, a FEM-based model for elastic image registration is proposed for the medical images that containing abnormal region due to pathological changes. Such model is based on uniform triangulation and Bayesian theorem. Firstly, the continuous domain corresponding to the size of the registered images is discreted by applying uniform triangulation. According to the characteristics of the elastic match, the basis function are constructed on the vertices of the every triangles. Next, the pixels of the image that containing pathological changes is classified by using the region growth algorithm. Based on the Bayes theorem, the FEM-based model is then received, and the model is naturally an energy function that respect to the basis function. Therefore, the problem of the image registration can be translated into the problem of finding the minimum value of the energy function. The gradient descent method is used to calculate the value in this article. Finally, the result of the simulated experiment show the effectiveness and accuracy of the addressed model.