Coupled Sparse Denoising and Unmixing With Low-Rank Constraint for Hyperspectral Image

Yang, Jingxiang; Zhao, Yongqiang; Chan, Jonathan Cheung-Wai; Kong, Seong G.

doi:10.1109/tgrs.2015.2489218

Cited by 81 publications

(41 citation statements)

References 42 publications

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“…Noise and blurring are common effects caused by distortion in HSI [24][25][26], so we add noise to the pristine HSI or blur it to simulate distorted HSIs. Figure 2 shows the sub-images added with different level of noise (Gaussian noise) and blurring (average filtering).…”

Section: Statistics Features In Spectral Domainmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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Abstract:Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method.

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Section: Statistics Features In Spectral Domainmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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“…High dimensional hyperspectral data admits low-rank and sparse representations owing to the very high correlation among spectral channels and spatial pixels [25,26]. For the hyperspectral remote sensing image denoising problem, the low-rank representation (LRR) based method has proven to be a powerful tool [19,20,27,28].…”

Section: Proposed Low-rank Representation Based Denoising Methodsmentioning

confidence: 99%

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Gao

Yao

et al. 2017

Remote Sensing

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Abstract:Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance.

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“…Since Huang's method in [29] has obtained the superior performance than DFN [12], NL [17], DRSM [27], and BS [29], so we only compare our method with Huang's method, AA model and Multiplicative-PM model (PM regularization term is applied in model (9) 3,5,6,8,9,11,12 report the certain corresponding enlarged details of the recovered images by Multiplicative-PM model, AA model, [29] and the proposed method. Fig.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…It first learns a dictionary from the noisy image patches, and then recovers each image patch by using the linear combinations of a few atoms in the learned dictionary. This method provides the state-of-the-art results, and it has been generalized to handle image sequence denoising, deblurring, decomposition, reducing artifacts [22][23][24][25][26][27][28]. For example, Zhao and Yang [28] proposed a hyperspectral image (HSI) denoising method by jointly utilizing sparse representation and low-rank constraint.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative noise removal via adaptive learned dictionaries and TV regularization

Wang

Zhao

2016

Digital Signal Processing

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Coupled Sparse Denoising and Unmixing With Low-Rank Constraint for Hyperspectral Image

Cited by 81 publications

References 42 publications

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Multiplicative noise removal via adaptive learned dictionaries and TV regularization

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