Robust Tensor Completion Using Transformed Tensor SVD
This work addresses robust tensor completion for applications such as image and video processing, but it is incremental as it builds on existing tensor SVD methods with a new transform approach.
The paper tackles robust tensor completion by introducing transformed tensor SVD, which uses unitary transform matrices instead of the discrete Fourier transform to achieve lower tubal rank, resulting in better recovery performance as measured by PSNR on datasets like hyperspectral, video, and face data.
In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices than that by using discrete Fourier transform matrix. This would be more effective for robust tensor completion. Experimental results for hyperspectral, video and face datasets have shown that the recovery performance for the robust tensor completion problem by using transformed tensor SVD is better in PSNR than that by using Fourier transform and other robust tensor completion methods.