Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence

Abstract

In recent studies, the tensor ring (TR) rank has shown high effectiveness in tensor completion due to its ability of capturing the intrinsic structure within high-order tensors. A recently proposed TR rank minimization method is based on the convex relaxation by penalizing the weighted sum of nuclear norm of TR unfolding matrices. However, this method treats each singular value equally and neglects their physical meanings, which usually leads to suboptimal solutions in practice. To alleviate this weakness, this paper proposes an enhanced low-rank sparsity measure, which can more accurately approximate the TR rank and better promote the low-rankness of the solution. In specific, we apply the logdet function onto TR unfolding matrices to shrink less the larger singular values while shrink more the smaller ones. To solve the proposed nonconvex model efficiently, we develop an alternating direction method of multipliers algorithm and theoretically prove that, under some mild assumptions, our algorithm converges to a stationary point. Extensive experiments on color images, multispectral images, and color videos demonstrate that the proposed method outperforms several state-of-the-art competitors in both visual and quantitative comparison.