In this article, we introduce a nonconvex tensor recovery approach, which employs the powerful ket augmentation technique to expand a low order tensor into a high-order one so that we can exploit the advantage of tensor train (TT) decomposition tailored for high-order tensors. Moreover, we define a new nonconvex surrogate function to approximate the tensor rank, and develop an auto-weighted mechanism to adjust the weights of the resulting high-order tensor's TT ranks. To make our approach robust, we add two mode-unfolding regularization terms to enhance the model for the purpose of exploring spatio-temporal continuity and self-similarity of the underlying tensors. Also, we propose an implementable algorithm to solve the proposed optimization model in the sense that each subproblem enjoys a closed-form solution. A series of numerical results demonstrate that our approach works well on recovering color images and videos.