Image processing is important on various fields to achieve various functions. In this paper two classes of
regularization strategies to achieve image recovery and reduce noise suppression from Original image in projection-based image
deblurring. Landweber iteration leads to a fixed level of regularization, which allows us to achieve fine-granularity control of
projection-based iterative deblurring by varying the value. Regularization filters can be gained by probing into their asymptotic
behavior—the fixed point of nonexpansive mappings. Different image structures (smooth regions, regular edges and textures)
are observed correspond to different fixed points of nonexpansive mappings when the temperature (regularization) parameter
varies. Such an analogy motivates us to propose a deterministic annealing based approach toward spatial adaptation in
projection-based image deblurring.
