fast tri-factorization method

Tensor completion is one of the ecient methods for restoring datasuch that minimizing the rank of the tensor leads to an appropriate solution.However, it gives a non-convex objective function, which generates an NPhardproblem. To overcome this problem, instead of using the rank function,the trace norm is applied. To solve this problem, Simple Low Rank TensorCompletion (SiLRTC) can be used. In the methods based on trace norm, theSingular Value Decomposition (SVD) is used, which increases computationalcomplexity of these methods with increasing dimensions. In order to reducethe computational complexity of SVD, the approximate SVD can be utilized.In this paper, to accelerate the convergence speed of SiLRTC Algorithm, thenew combined method FTF-SiLRTC is presented. On the other hand, theimages recovered using the mentioned algorithms are generally accompaniedby horizontal and vertical noise lines and have low accuracy. To solve thisdiculty, the total variation (TV) regularization is added to the problem andthe FTF-SiLRTC-TV Algorithm is introduced to solve it with higher accuracy.