Super resolution MIA reconstruction

Super resolution MIA reconstruction is a method to obtain a high-resolution image from multiple degraded and low-resolution images through a certain signal processing technology. The maximum a posteriori probability (map) method and the convex set projection mapping (POCS) method based on Bayes estimation are the most effective methods. The projection mapping method of convex sets was first proposed by startk et al. In fact, it is an iterative restoration method that introduces prior information into the restoration process. In this method, prior knowledge is used as the constraint of the solution, which is limited to a closed convex set, and iterative method is used to solve the problem. The utility model has the advantages of simple structure and convenient solution. When the posterior probability density of the original image is known, the map method based on Bayes estimation has a good application. Schultz et al. Used map method to solve the super-resolution restoration problem of video sequence images. Using hubermarkov GBS prior model, the restoration problem was transformed into a constrained optimization problem with unique solution. Maximum likelihood estimation (ML) is a special case of map estimation. Katasagge Los et al. Use this method to estimate sub-pixel displacement and image noise at the same time, and use the maximum expectation method to solve the problem of ML estimation, which has achieved good restoration effect.