Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation

• URL: http://arxiv.org/abs/2005.04143v1
• Date: Fri, 8 May 2020 16:42:18 GMT
• Title: Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
• Authors: Haijin Zeng, Xiaozhen Xie, Jifeng Ning
• Abstract summary: Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs)
• Score: 1.3406858660972554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR matrix is approximated using nuclear norm (NN). The NN is defined by adding all singular values together, which is essentially a $L_1$-norm of the singular values. It results in non-negligible approximation errors and thus the resulting matrix estimator can be significantly biased. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose a spatial-spectral TV (SSTV) regularized non-convex local LR matrix approximation (NonLLRTV) method to remove mixed noise in HSIs. From one aspect, local LR of HSIs is formulated using a non-convex $L_{\gamma}$-norm, which provides a closer approximation to the matrix rank than the traditional NN. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial domain. The TV regularization is effective in preserving the smoothness and removing Gaussian noise. These facts inspire the integration of the NonLLR with TV regularization. To address the limitations of bandwise TV, we use the SSTV regularization to simultaneously consider global spatial structure and spectral correlation of neighboring bands. Experiment results indicate that the use of local non-convex penalty and global SSTV can boost the preserving of spatial piecewise smoothness and overall structural information.

Related papers

• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising [16.562236225580513]
  We propose a new TV-type regularization called Graph-SSTV (GSSTV) for mixed noise removal. GSSTV generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator based on this graph. We demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal.
  arXiv  Detail & Related papers  (2022-07-22T12:46:21Z)
• Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising [11.79762223888294]
  We propose a novel TV regularization to simultaneously characterize the sparsity and low-rank priors of the gradient map (LRSTV) The new regularization not only imposes sparsity on the gradient map itself, but also penalizes the rank on the gradient map after Fourier transform. It naturally encodes the sparsity and lowrank priors of the gradient map, and thus is expected to reflect the inherent structure of the original image more faithfully.
  arXiv  Detail & Related papers  (2022-04-27T12:31:55Z)
• Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization [49.55649406434796]
  We propose a novel non particular approach to robust principal component analysis for HSI denoising. We develop accurate approximations to both rank and sparse components. Experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-01-08T11:48:46Z)
• Benign Overfitting of Constant-Stepsize SGD for Linear Regression [122.70478935214128]
  inductive biases are central in preventing overfitting empirically. This work considers this issue in arguably the most basic setting: constant-stepsize SGD for linear regression. We reflect on a number of notable differences between the algorithmic regularization afforded by (unregularized) SGD in comparison to ordinary least squares.
  arXiv  Detail & Related papers  (2021-03-23T17:15:53Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)
• Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization [42.56231647066719]
  Hyperspectral image (HSI) has some advantages over natural image for various applications due to the extra spectral information. During the acquisition, it is often contaminated by severe noises including Gaussian noise, impulse noise, deadlines, and stripes. We present a HSI restoration method named smooth and robust low rank tensor recovery.
  arXiv  Detail & Related papers  (2020-06-29T02:10:07Z)
• Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation [1.3406858660972554]
  Noise contamination can often be during and conversion penalty indicate smooth local domains. We propose a novel approximation total variation (SSTV) to restore this aspect. Results can boost the overall structural information in HSIs.
  arXiv  Detail & Related papers  (2020-05-30T10:03:39Z)
• Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment [52.02794488304448]
  We propose a new distribution alignment method based on a log-likelihood ratio statistic and normalizing flows. We experimentally verify that minimizing the resulting objective results in domain alignment that preserves the local structure of input domains.
  arXiv  Detail & Related papers  (2020-03-26T22:10:04Z)
• Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning [70.57193072829288]
  Low-rank Multi-view Subspace Learning has shown great potential in cross-view classification in recent years. Existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously. We propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy.
  arXiv  Detail & Related papers  (2020-03-22T03:57:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.