Low-rank quaternion tensor completion for recovering color videos and images

作者：

Highlights：

• Low-rank quaternion tensor completion method, a novel approach to estimate missing pixels in color videos and images is proposed.

• We respectively reconstruct a color image and a color video as a quaternion matrix (second-order tensor) and a third-order quaternion tensor by encoding the red, green, and blue channel pixel values on the three imaginary parts of a quaternion.

• Under the definition of Tucker rank, the global low-rank prior to quaternion tensor is encoded as the nuclear norm of unfolding quaternion matrices.

• Theoretically, the proposed method can be well used to recover missing entries of any multidimensional data with color structures.

摘要

•Low-rank quaternion tensor completion method, a novel approach to estimate missing pixels in color videos and images is proposed.•We respectively reconstruct a color image and a color video as a quaternion matrix (second-order tensor) and a third-order quaternion tensor by encoding the red, green, and blue channel pixel values on the three imaginary parts of a quaternion.•Under the definition of Tucker rank, the global low-rank prior to quaternion tensor is encoded as the nuclear norm of unfolding quaternion matrices.•Theoretically, the proposed method can be well used to recover missing entries of any multidimensional data with color structures.

论文关键词：Quaternion,Color videos,Color images,Tensor completion,Low-rank

论文评审过程：Received 14 November 2019, Revised 25 May 2020, Accepted 12 June 2020, Available online 19 June 2020, Version of Record 25 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107505