Quaternion tensor completion with sparseness for color video recovery

A novel low -rank completion algorithm based on the quaternion tensor is proposed in this paper. This approach uses the TQt-rank of quaternion tensor to maintain the structure of RGB channels throughout the entire process. In more detail, the pixels in each frame are encoded on three imaginary parts of a quaternion as an element in a quaternion matrix. Each quaternion matrix is then stacked into a quaternion tensor. A logarithmic function and truncated nuclear norm are employed to characterize the rank of the quaternion tensor in order to promote the low rankness of the tensor. Moreover, by introducing a newly defined quaternion tensor discrete cosine transform -based (QTDCT) regularization to the low -rank approximation framework, the optimized recovery results can be obtained in the local details of color videos. In particular, the sparsity of the quaternion tensor is reasonably characterized by ??????1 norm in the QDCT domain. This strategy is optimized via the twostep alternating direction method of multipliers (ADMM) framework with convergence analysis. Numerical experimental results for recovering color videos show the obvious advantage of the proposed method over other potential competing approaches.