Power of $\ell_1$-Norm Regularized Kaczmarz Algorithms for High-Order Tensor Recovery
This work addresses the need for fast optimization algorithms for high-dimensional tensor data in applications such as image and video processing, representing an incremental improvement with hybrid methods.
The authors tackled the problem of recovering high-order tensors with sparse or low-rank structures by proposing novel Kaczmarz algorithms with ℓ₁-norm regularization, resulting in effective performance in image and video processing tasks like destriping and deconvolution.
Tensors serve as a crucial tool in the representation and analysis of complex, multi-dimensional data. As data volumes continue to expand, there is an increasing demand for developing optimization algorithms that can directly operate on tensors to deliver fast and effective computations. Many problems in real-world applications can be formulated as the task of recovering high-order tensors characterized by sparse and/or low-rank structures. In this work, we propose novel Kaczmarz algorithms with a power of the $\ell_1$-norm regularization for reconstructing high-order tensors by exploiting sparsity and/or low-rankness of tensor data. In addition, we develop both a block and an accelerated variant, along with a thorough convergence analysis of these algorithms. A variety of numerical experiments on both synthetic and real-world datasets demonstrate the effectiveness and significant potential of the proposed methods in image and video processing tasks, such as image sequence destriping and video deconvolution.