Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization

• URL: http://arxiv.org/abs/2111.07044v1
• Date: Sat, 13 Nov 2021 05:30:56 GMT
• Title: Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization
• Authors: Hang Zhou, Yanchi Su, Zhanshan Li
• Abstract summary: We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Experiments demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.
• Score: 10.131033322742363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the low-rank property of different components extracted from the image has been considered in man hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial self-similarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy computational burden issues due to the involvement of singular value decomposition operation on matrix and tensor in the original high-dimensionality space of HSI. We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Specifically, to employ the GSC among spectral bands, the noisy HSI is projected into a low-dimensional subspace which simplified calculation. After that, a weighted low-rank tensor regularization term is introduced to characterize the priors in the reduced image subspace. Moreover, we design an algorithm based on alternating minimization to solve the nonconvex problem. Experiments on simulated and real datasets demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.

Related papers

• Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation [71.69331824668954]
  Low-rank tensor representation is an important approach to alleviate spectral variations. Previous low-rank representation methods can only be applied to the regular data cubes. We propose a novel irregular lowrank representation method that can efficiently model the irregular 3D cubes.
  arXiv  Detail & Related papers  (2024-10-24T02:56:22Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• 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)
• 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)
• FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal [20.043152870504738]
  This paper introduces a fast and parameter-free hyperspectral image mixed noise removal method (termed FastHyMix) It exploits two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. The proposed method takes advantage of the low-rankness using subspace representation and the correlation of HSIs by adding a powerful deep image prior.
  arXiv  Detail & Related papers  (2021-09-18T08:35:45Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising [10.597014770267672]
  Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between observed noisy images and underlying clean images. We introduce a novel model guided interpretable network for HSI denoising.
  arXiv  Detail & Related papers  (2020-12-03T11:05:01Z)
• 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)

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.