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In recent years, image restoration has gradually become the focus of research in the field of digital image processing. In order to effectively restore the damaged and lost image data, the image data to be recovered is expressed in the form of tensor data structure, Based on the tensor-based image restoration method, by fully recognizing the inherent structure of the mixed noise in the clean image. Specifically, for the clean image content, tensor Tucker decomposition is used to describe the global correlation between all bands, and the anisotropic spatial spectral total change (SSTV) regularization to characterize the spatial and frequency segments Smooth structure. For the content of mixed noise, regularization is used to detect sparse noise, including fringe, impulse noise and dead pixels, an effective method for solving the obtained optimization problem by using the enhanced Lagrangian Multiplier (ALM) method is developed algorithm. Finally, the simulation results show that the algorithm works well and the convergence is fast, which can restore the damaged image to a good state, and the recovery precision is high.