Paper Info
Title
Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm
Abstract
During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step. In HSI denoising tasks, the method based on low-rank prior has achieved satisfying results. Among numerous denoising methods, the tensor nuclear norm (TNN), based on the tensor singular value decomposition (t-SVD), is employed to describe the low-rank prior approximately. Its calculation can be sped up by the fast Fourier transform (FFT). However, TNN is computed by the Fourier transform, which lacks the function of locating frequency. Besides, it only describes the low-rankness of the spectral correlations and ignores the spatial dimensions' information. In this paper, to overcome the above deficiencies, we use the basis redundancy of the framelet and the low-rank characteristics of HSI in three modes. We propose the framelet-based tensor fibered rank as a new representation of the tensor rank, and the framelet-based three-modal tensor nuclear norm (F-3MTNN) as its convex relaxation. Meanwhile, the F-3MTNN is the new regularization of the denoising model. It can explore the low-rank characteristics of HSI along three modes that are more flexible and comprehensive. Moreover, we design an efficient algorithm via the alternating direction method of multipliers (ADMM). Finally, the numerical results of several experiments have shown the superior denoising performance of the proposed F-3MTNN model.
Year
DOI
Venue
2021
10.3390/rs13193829
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral images, denoising, framelet, tensor nuclear norm
Journal
13
Issue
Citations 
PageRank 
19
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Wenfeng Kong110.69
Yangyang Song200.34
Jing Liu318757.41