Natural image denoising method based on regionalism and dictionary learning

The invention discloses a natural image denoising method based on regionalism and dictionary learning. The natural image denoising method based on the regionalism and the dictionary learning mainly solves the problems that in an image denoising method based on kernel singular value decomposition (KSVD), blurring occurs in a weak texture region and fake texture occurs in a smooth region. The realization scheme includes that: removing high-frequency information of a noise-contained image through alternation of a stationary wavelet, and extracting structural information through a primal sketch algorithm, dividing the noise-contained image into three regions including a structural region, a texture region and a smooth region; obtaining a dictionary of the structural region and the texture region through a KSVD method; denoising the three regions respectively, merging denoising results, and obtaining a denoising image. An idea of combination of the regionalism and the dictionary learning is utilized, a dictionary which is obtained by the dictionary learning is enabled to conduct sparse presentation on corresponding signal composition of the image , information of edges and texture of the image is kept effectively, a denoising effect is improved, and the natural image denoising method can be used for obtaining high-quality images from noise-contained low-quality images.