Research On Image Segmentation Problem Based On The Smoothing Total Variation Model

As one of the classic image segmentation models,the GAC model has received widespread attention in the field of image processing.However,the segmentation results of this model are still not satisfactory.In this thesis we propose a modified model based on the GAC model to research and discuss the various image segmentation problem.Specifically,this thesis is organized as follows.In chapter 1,we introduce some research backgrounds and methods for digital image processing and image segmentation,and also introduce main contribution of the thesis.In chapter 2,we give some preliminary knowledge,including some classic models,the primal-dual method,the steepest descent method,the variational level set method and some notations of mathematical symbols which used in the thesis.In chapter 3,we mainly propose a new image segmentation model,and give some related theory analysis.In addition,we propose a efficient method to solve the established model.That's to say,turn the original problem into the primal dual problem to solve the new model quickly and efficiently.The numerical comparison validates the validity of the model and the algorithm.The last chapter summarizes some main results in this thesis and points out some future research topics.