Iterative Methods for Computing the T-Square Root of Third-Order Tensors
This work provides new iterative algorithms for tensor square roots with convergence proofs, benefiting image processing tasks that require cross-channel decorrelation and structural preservation.
The authors develop Newton and Denman-Beavers iterations for computing the principal square root of third-order tensors under the T-product, with convergence guarantees. Applied to image processing, their tensor-based methods (e.g., T-Whitening, color transfer) show improved structural preservation and cross-channel decorrelation over classical methods.
We develop and analyze iterative methods for computing the principal square root of third-order tensors under the T-product framework. Tensor extensions of the Newton iteration (quadratic convergence) and the Denman--Beavers iteration (geometric convergence with simultaneous computation of the inverse square root) are proposed, with rigorous convergence guarantees established via the Fourier-domain block-diagonalization of the T-product. We apply these methods to image processing, introducing Tensor Decorrelated Grayscale conversion, T-Whitening, and optimal color transfer under the T-product geometry. We also formulate the Tensor Bures--Wasserstein distance and prove it defines a valid metric on the space of T-positive definite tensors. Numerical experiments confirm rapid convergence and demonstrate that the proposed tensor-based techniques offer improved structural preservation and cross-channel decorrelation compared to classical methods.