Multidimensional wiener filtering using fourth order statistics of hyperspectral images

Letexier, Damien; Bourennane, Salah

doi:10.1109/icassp.2008.4517760

Cited by 14 publications

(10 citation statements)

References 7 publications

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“…Third, the noise intensity of different bands is different, which is not considered in (9). To overcome these drawbacks, a new regularization is proposed in this paper.…”

Section: A Kernel Regularization In Hsi Denoising Modelmentioning

confidence: 94%

“…However, there are three drawbacks in (9). First, it does not take advantage of the spectral information.…”

Section: A Kernel Regularization In Hsi Denoising Modelmentioning

confidence: 97%

“…Specifically, we propose two strategies to overcome the drawbacks in (9). In the first strategy, the distance computed as the similarity should be changed according to the characteristic of HSI.…”

Section: B Spectral-spatial Kernel Modelmentioning

confidence: 99%

“…This type of method [9]- [11] extends traditional 1-D or 2-D denoising filters to multidimensional data by considering an HSI as a 3-D array. For example, Letexier and Bourennane proposed a multidimensional Wiener filtering (MWF) [9], [11] which modeled an HSI as a third-order tensor and reduced noise based on tensor analysis [12], [13] to keep data as a whole entity. Herrero [14] proposed a 3-D anisotropic filtering to enhance the HSI, and Wang et al [15] proposed an alternative anisotropic diffusion scheme which took the specifics of HSIs into account.…”

Section: Introductionmentioning

confidence: 99%

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Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Yuan

Zheng

2015

IEEE Trans. Geosci. Remote Sensing

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Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral-spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral-spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral-spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral-spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data.Index Terms-Adaptive kernel, hyperspectral image (HSI) denoising, nonlocal means (NLM), spectral-spatial kernel regularization.

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“…Third, the noise intensity of different bands is different, which is not considered in (9). To overcome these drawbacks, a new regularization is proposed in this paper.…”

Section: A Kernel Regularization In Hsi Denoising Modelmentioning

confidence: 94%

“…However, there are three drawbacks in (9). First, it does not take advantage of the spectral information.…”

Section: A Kernel Regularization In Hsi Denoising Modelmentioning

confidence: 97%

Section: B Spectral-spatial Kernel Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Yuan

Zheng

2015

IEEE Trans. Geosci. Remote Sensing

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“…For example, a spatial-spectral adaptive total variation model is proposed in [12]; a cubic TV (CTV) model is designed in [13]; a nonlocal sparse representation framework is developed in [14]. In addition, as a data cube, the tensor filtering technique is exploited for HSI denoising, such as multidimensional Wiener filtering (MWF) [15] and Block-Matching 4D filtering (BM4D) [16]. However, due to the lack of the consideration of deeper prior knowledge, these methods often result in suboptimal performance in eliminating mixed noise.…”

Section: Introductionmentioning

confidence: 99%

Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

2020

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Hyperspectral images (HSIs) denoising aims at recovering noise-free images from noisy counterparts to improve image visualization. Recently, various prior knowledge has attracted much attention in HSI denoising, e.g., total variation (TV), low-rank, sparse representation, and so on. However, the computational cost of most existing algorithms increases exponentially with increasing spectral bands. In this paper, we fully take advantage of the global spectral correlation of HSI and design a unified framework named subspace-based Moreau-enhanced total variation and sparse factorization (SMTVSF) for multispectral image denoising. Specifically, SMTVSF decomposes an HSI image into the product of a projection matrix and abundance maps, followed by a ‘Moreau-enhanced’ TV (MTV) denoising step, i.e., a nonconvex regularizer involving the Moreau envelope mechnisam, to reconstruct all the abundance maps. Furthermore, the schemes of subspace representation penalizing the low-rank characteristic and ℓ 2 , 1 -norm modelling the structured sparse noise are embedded into our denoising framework to refine the abundance maps and projection matrix. We use the augmented Lagrange multiplier (ALM) algorithm to solve the resulting optimization problem. Extensive results under various noise levels of simulated and real hypspectral images demonstrate our superiority against other competing HSI recovery approaches in terms of quality metrics and visual effects. In addition, our method has a huge advantage in computational efficiency over many competitors, benefiting from its removal of most spectral dimensions during iterations.

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Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising

Wang

Jin

et al. 2017

Signal Processing

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Multidimensional wiener filtering using fourth order statistics of hyperspectral images

Cited by 14 publications

References 7 publications

Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising

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