Hyperspectral image restoration via hybrid smoothness regularized auto-weighted low-rank tensor ring factorization - Luo, Xuegang; Lv, Junrui; Wang, Bo; Liu, Dujin; Wang, Juan (Frontiers in Earth Science, 2023)
Restoration of hyperspectral images (HSI) is a crucial step in many potential applications as a preprocessing step. Recently, low-rank tensor ring factorization based on high-order tensors' powerful and generalized representation ability was applied for HSI reconstruction. However, these TR-based methods, neglecting the difference in low-rank characteristics among different bands and the high-order structure of HSIs, usually lead to suboptimal solutions. In this article, we propose a novel Auto-weighted low-rank Tensor Ring Factorization with Hybrid Smoothness regularization (ATRFHS) for mixed noise removal in HSI. Patch multi-way delay embedding transform is leveraged to transform HSI data into higher-order tensors. TR factorization using latent factors rank minimization removes the mixed noise in HSI data. To highlight nuclear norms of factor tensors differently effective, an auto-weighted strategy is employed to reduce the more prominent factors while shrinking the smaller ones. A hybrid regularization combining total variation (TV) and phase congruency (PC) is incorporated into a low-rank tensor ring factorization model for the HSI noise removal problem. This efficient combination yields sharper edge preservation and resolves this weakness of existing pure TV regularization. Moreover, we develop an efficient algorithm for solving the resulting optimization problem using the alternating direction multipliers method (ADMM). Extensive experimental results demonstrate that our proposed method can significantly outperform existing approaches for mixed noise removal in HSI. The proposed algorithm is validated on synthetic and natural HSI data.