T-Hy-Demosaicing: Hyperspectral Reconstruction Via Tensor Subspace Representation Under Orthogonal Transformation

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.4842-4853
• Main Authors: Xu, Shan-Shan, Huang, Ting-Zhu, Lin, Jie, Chen, Yong
• Format: Article
• Language: English
• Subjects: Algorithms; Hyperspectral demosaicing; Hyperspectral imaging; Image coding; Image color analysis; Imagery; Mathematical analysis; Matrix decomposition; Periodic structures; Pollution measurement; proximal alternating minimization (PAM); Representations; Self-similarity; Sensors; Singular value decomposition; Subspaces; tensor subspace representation; Tensors; tran-based tensor singular value decomposition (t-SVD)
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3076793

This article aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of subsampling to estimate a fixed subspace in matrix form from the measurement result, which reduces the representation ability of the subspace in iterations and destroys the intrinsic structure of the tensor. To overcome these drawbacks, we propose a tensor-based HSI demosaicing (T-Hy-demosaicing) model with tensor subspace representation, which takes the low-tubal-rankness and the nonlocal self-similarity into account. In particular, we suggest a tensor singular value decomposition based on orthogonal transformation (Tran-based t-SVD) to learn the tensor subspace that possesses a more powerful representation ability. In addition, we develop an effective algorithm to solve the proposed nonconvex model under the framework of the proximal alternating minimization algorithm. Experiments conducted on simulated datasets illustrate that the proposed method outperforms other comparative methods in both visual and quantitative terms.