Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang

doi:10.1109/tgrs.2014.2385082

Cited by 78 publications

(28 citation statements)

References 43 publications

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“…This approach exhibited good denoising performance but can be improved by adaptively adjusting the regularization parameter on which the denoising performance is highly dependent. Yuan et al in [40] proposed also another denoising method where the regularization term in the cost function is often approached by a kernel model. However, this approach has three main drawbacks when applied for multichannel image denoising.…”

Section: Related Workmentioning

confidence: 99%

Multispectral image denoising with optimized vector non-local mean filter

Said

Hadjidj

Melkemi

et al. 2016

Digital Signal Processing

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Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with additive white Gaussian noise and PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity.

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Section: Related Workmentioning

confidence: 99%

Multispectral image denoising with optimized vector non-local mean filter

Said

Hadjidj

Melkemi

et al. 2016

Digital Signal Processing

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“…Apparently, as mentioned in the article [15], kernel-based methods are also based on statistical hypothesis test, and inherit the shortcomings of traditional target detection methods. It can be concluded that kernel-based methods attempt to find a stable and credible feature space (distance metric) for separating potential target pixels and background ones [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints

Dong

Zhang

et al. 2018

Remote Sensing

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By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target samples and preserving the discriminative information is also a challenging problem in hyperspectral target detection. To overcome these shortcomings, this paper proposes a novel adaptive information-theoretic metric learning with local constraints (ITML-ALC) for hyperspectral target detection. The proposed method firstly uses the information-theoretic metric learning (ITML) method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs without certain assumptions, needing fewer adjusted parameters. Then, adaptively local constraints are applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, target detection decision can be made by considering both the threshold and the changes between the distances before and after metric learning. Experimental results demonstrate that the proposed method can obviously separate target samples from background ones and outperform both the state-of-the-art target detection algorithms and the other classical metric learning methods.

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“…Moreover, the combination of sparse reduced-rank regression and wavelet transform is another way to produce appealing results [21]. Except for wavelet transform-based methods, there are still a number of techniques developed for hyperspectral restoration, e.g., tensor decomposition methods [22,23], sparse representation [24,25] or sparse dictionary learning methods [26,27], kernel-based methods [28], deep learning [29] or neural network [30,31] methods and Bayesian methods [32,33]. These methods have been proved to produce outstanding results.…”

Section: Introductionmentioning

confidence: 99%

A Novel 3D Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising

Sun

Zhan

2018

IJGI

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Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothness and low rank has yielded promising reconstruction results. In this paper, we propose a novel mixed-noise removal method by employing 3D anisotropic total variation and low rank constraints simultaneously for the problem of hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in an HSI lies in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rankness of an HSI is approximately exploited by the nuclear norm, while the spectral-spatial smoothness is explored using 3D anisotropic total variation (3DATV), which is defined as a combination of 2D spatial TV and 1D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by the alternating direction method of multipliers (ADMM). Experimental results of both simulated and real HSI datasets validate the superior performance of the proposed method in terms of quantitative assessment and visual quality.

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Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising

Cited by 78 publications

References 43 publications

Multispectral image denoising with optimized vector non-local mean filter

Multispectral image denoising with optimized vector non-local mean filter

Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints

A Novel 3D Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising

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