In this paper, we develop a regional spatially adaptive total variation model. Initially, the spatial information is extracted based on each pixel, and then two filtering processes are added to suppress the effect of pseudo edges. In this paper, we create a provincial spatially versatile aggregate variety model. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudo edges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, and keep up the fractional smoothness of the high-determination picture.