  Low-rank tensor completion (LRTC) is an important problem in computer vision
and machine learning. The minimax-concave penalty (MCP) function as a
non-convex relaxation has achieved good results in the LRTC problem. To makes
all the constant parameters of the MCP function as variables so that futherly
improving the adaptability to the change of singular values in the LRTC
problem, we propose the bivariate equivalent minimax-concave penalty (BEMCP)
theorem. Applying the BEMCP theorem to tensor singular values leads to the
bivariate equivalent weighted tensor $\Gamma$-norm (BEWTGN) theorem, and we
analyze and discuss its corresponding properties. Besides, to facilitate the
solution of the LRTC problem, we give the proximal operators of the BEMCP
theorem and BEWTGN. Meanwhile, we propose a BEMCP model for the LRTC problem,
which is optimally solved based on alternating direction multiplier (ADMM).
Finally, the proposed method is applied to the data restorations of
multispectral image (MSI), magnetic resonance imaging (MRI) and color video
(CV) in real-world, and the experimental results demonstrate that it
outperforms the state-of-arts methods.
