We consider the problem of tensor (i.e., multidimensional array) inpainting in this paper. By using higher-order singular value decomposition, we propose an iterative algorithm that performs soft thresholding on entries of the core tensor and then reconstructs via the directional orthogonal matrices. An inpainted tensor is obtained at the end of the iteration. Simulations conducted over color images, video frames, and MR images validate that the proposed algorithm is competitive with state-of-the-art completion algorithms. The evaluation is made in terms of quality metrics and visual comparison. [ABSTRACT FROM AUTHOR]
Compressed sensing ensures the accurate reconstruction of sparse signals from far fewer samples than required in the classical Shannon-Nyquist theorem. In this paper, a generalized hard thresholding pursuit (GHTP) algorithm is presented that can recover unknown vectors without the sparsity level information. We also analyze the convergence of the proposed algorithm. Numerical experiments are given for synthetic and real-world data to illustrate the validity and the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]
This paper proposes improved delay-range-dependent H performance conditions and a H filtering algorithm for linear systems with interval time-varying delays. The proposed filtering approach guarantees that the results are less conservative than those obtained by other existing approaches. Numerical examples well demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]