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Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising

arXiv 2106.12489 ↗First posted 23 Jun 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

Low-rankness is important in the hyperspectral image (HSI) denoising tasks. The tensor nuclear norm (TNN), defined based on the tensor singular value decomposition, is a state-of-the-art method to describe the low-rankness of HSI. However, TNN ignores some physical meanings of HSI in tackling denoising tasks, leading to suboptimal denoising performance. In this paper, we propose the multi-modal and frequency-weighted tensor nuclear norm (MFWTNN) and the non-convex MFWTNN for HSI denoising tasks. Firstly, we investigate the physical meaning of frequency slices and reconsider their weights to improve the low-rank representation ability of TNN. Secondly, we consider the correlation among two spatial dimensions and the spectral dimension of HSI and combine the above improvements to TNN to propose MFWTNN. Thirdly, we use non-convex functions to approximate the rank function of the frequency tensor and propose the NonMFWTNN to relax the MFWTNN better. Besides, we adaptively choose bigger weights for slices mainly containing noise information and smaller weights for slices containing profile information. Finally, we develop the efficient alternating direction method of multiplier (ADMM) based algorithm to solve the proposed models, and the effectiveness of our models are substantiated in simulated and real HSI datasets.

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References · 18

  1. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  2. 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 110
  3. 2015Exact tensor completion using t-SVDcited by 70
  4. 2017Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decompositioncited by 61
  5. 2018Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Networkcited by 51
  6. 20203D Quasi-Recurrent Neural Network for Hyperspectral Image Denoisingcited by 44
  7. 2021Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representationscited by 39
  8. 2013Square Deal: Lower Bounds and Improved Relaxations for Tensor Recoverycited by 37
  9. 2019Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completioncited by 36
  10. 2017Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear normcited by 19
  11. 2018Tensor N-tubal rank and its convex relaxation for low-rank tensor recoverycited by 19
  12. 2020Dictionary Learning with Low-rank Coding Coefficients for Tensor Completioncited by 19
6 more
  1. 2016Fast L1-L2 minimization via a proximal operatorcited by 16
  2. 2021Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recoverycited by 16
  3. 2021Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priorscited by 12
  4. 2020Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximationcited by 10
  5. 2019A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decompositioncited by 6
  6. 2020Frequency-Weighted Robust Tensor Principal Component Analysiscited by 3

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