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Semi-Tensor Product-Based Multi-Term Randomized T-SVD and Its Visual Applications

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing high-dimensional visual data such as color images and videos. However, the standard t-product imposes strict dimensional compatibility constraints. Although extensions based on the semi-tensor product (STP) relax this restriction, their single-term formulations still suffer from limited approximation accuracy. Moreover, these deterministic methods incur high computational costs when processing large-scale tensor data. To address these issues, thi

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