Research On Image Restoration Based On Fixed Point Iterative Algorithm

Image restoration is an important research area in the field of image processing.Its main process is to restore the original image according to a regression model.In this paper, we mainly apply the proximity operator to the image denoising model,and then improve the model. We construct a second-order derivative model and a mixed parameter model. The fixed point iterative algorithm is used to volidate them.Its main contents include: The first chapter mainly introduces the research background and significance of image process. And it briefly introduces several important aspects and main branches of image processing. The research status of image restoration are also introduced.In the second chapter, fixed point iteration algorithm is introduced. We give out the fixed point iterative algorithm of ROF model.Then the ROF model is improved, and a new model is constructed by using the second-order derivative to replace the regular term in the ROF model. The fixed point iteration algorithm and convergence condition are given,And to verify and compare. In the fourth chapter, we use the first-order derivative and the second-order derivative to establish the mixed parameter model, and give the convergence condition of the fixed point iteration algorithm. The numerical experiments are carried out. The experimental results show that The new model has improved the denoising effect compared with the traditional denoising model.