The application of computer image processing technology is more and more extensive, such as geological survey, disaster inspection, urban planning medical imaging, industry, agriculture, military and other fields. In the method of image denoising, the restoration model based on variational principle has been the focus of research in the field of digital image. In order to take into account the recovery of image edge and texture information and a more efficient optimization algorithm, a fractal-order total variational denoising method based on shearlet regularization terms is proposed in this paper. This model combines the advantages of fractional variational model and shear wave transform, which can not only effectively recover the edge and other details of the image, but also enhance the mid-frequency information of the image by using more adjacent pixel information, and retain the low-frequency component of the image nonlinearly. Thus, anisotropic features such as edges and curves of the image can be effectively preserved. In numerical calculation, by applying alternating direction method of multiplier (ADMM) to solve this model, we find that all ADMM subproblems have closed-form solutions. Finally, extensive experiments involving both denoising and texture extraction applications are employed to illustrate the validity of this model.