Hyperspectral image restoration via hybrid smoothness regularized auto-weighted low-rank tensor ring factorization

Abstract

Restoration of hyperspectral images (HSI) is a crucial step in many potential applications as a preprocessing step. Recently, low-rank tensor ring factorization was applied for HSI reconstruction, which has high-order tensors’ powerful and generalized representation ability. Although low-rank TR-based approaches with nuclear norm regularization achieved successful results for restoring hyperspectral images, there is still room for improved tensor low-rank approximation. 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. Nonlocal Cuboid Tensorization (NCT) is leveraged to transform HSI data into high-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 framework of alternating minimization. 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.