Adaptive image processing algorithm based on bi-dimensional local mean decomposition

In this paper, a Bi-dimensional Local Mean Decomposition (BLMD) algorithm with adaptive characteristics is proposed. The two-dimensional local mean decomposition algorithm can decompose the source image into multiple bi-dimensional production function components (BPF). The basic idea is to first obtain the extreme points in the decomposition process by the variable neighborhood window method, and then use the fractal theory to interpolate the image. And get the corresponding information of the mean surface. Then, the number of non-coincident extreme points on the zero-value plane projection between adjacent surfaces in the sieving process is statistically analyzed and analyzed, and the stopping condition corresponding to the characteristics of the image is given. It ensures that the BPF component obtained by the decomposition can truly reflect certain types of feature information of the image. Finally, it forms the two-dimensional local mean decomposition algorithm proposed in this paper. The empirical analysis shows that the method can adaptively decompose the image.