Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Zhuang, Lina; Ng, Michael K.; Fu, Xiouhua

doi:10.3390/rs13204098

Cited by 19 publications

(7 citation statements)

References 52 publications

(101 reference statements)

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“…In addition, the runtime (seconds) is also considered as the index to compute complexity. Especially, five state-of-the-art methods are employed as the baselines to conduct performance comparison, including deep-learningbased HySuDeep (Subspace representation and deep CNN Image prior) [15], tensor-decomposition-based LRTDGS (Weighted group sparsity-regularized low-rank tensor decomposition) [24], matrix-factorization-based E-3DTV (Enhanced 3-D total variation) [36], SNLRSF (Subspace-based nonlocal low-Rank and sparse factorization method) [33] and F-LRNMF (Framelet-regularized low-rank nonnegative matrix factorization method) [34].…”

Section: Experimental Simulations and Performance Analysismentioning

confidence: 99%

“…In the aspect of supervised methods, Xie et al proposed a convolution neural network with trainable nonlinear functions for hyperspectral image restoration [14]. Zhuang et al [15] adopted a fast and flexible denoising convolutional neural network to capture high local correlation in spatial subspace. However, deep learning based methods often involve a great deal of data training, as well as hyperparameters, which will lead to an increase in computational burden [16], [17].…”

Section: Introductionmentioning

confidence: 99%

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Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Liu

Yang

et al. 2023

IEEE Access

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Hyperspectral images captured by remote-sensing satellites are easily corrupted by various types of noise. Generally, hyperspectral signatures appear to be scattered in spatial-spectral domain, as well as noise. In transform domain, however, the principal components of a image are often centralized in the low-frequency band, while noise and some details are mainly contained in high-frequency components. The traditional transformation domain based smoothing methods take no account of the respective information distribution carried by different frequency bands. Hyperspectral image restoration aims to remove mixed noise for clean data, which usually amounts to an ill-posed inverse problem. Moreover, the matrixdecomposition-based model needs to reshape hyperspectral datacube into matrix form, which will lead to certain loss of spatial structure information. In order to address these issues, this paper incorporates piecewise weighted smoothing regularization in tight framelet domain to reformulate a novel convex model for hyperspectal image restoration, which maintains image signature to a great extent. Based on the noise-perturbed degradation model, the smoothing regularizer in tight framelet domain is imposed on abundance signature, in which the transformation matrix, as well as the weighted coefficient, is well designed for different frequency bands. As a surrogate of sparsity, the l q -norm is employed to denoise for the enhancement of hyperspectral signatures. To the end, an efficient solver is carefully designed to derive the closed-form solutions by proximal alternating optimization. The experimental results on several synthetic and real datasets, not only demonstrate that the performance of proposed method is better than that of the current state-of-the-art approaches, but also verify the validation of regularization terms for hyperspectral image restoration.INDEX TERMS Hyperspectral image restoration, tight framelet domain, piecewise weighted smoothing;degradation model, mixed noise

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Section: Experimental Simulations and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Liu

Yang

et al. 2023

IEEE Access

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“…Compared with NGmeet, TenSRDe [29] can be viewed as a tensor version of such a NSR-based HSI denoising method. More related works refer to the articles presented in [24], [27], [38], and [39]. To the best of our knowledge, these NSR-related methods are nearly state-of-the-art and also very fast compared with the previous SR-unrelated methods.…”

Section: B Nsr-related Workmentioning

confidence: 99%

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Wang

Peng

Cao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal denoiser but ignore the understanding why these pseudoimages have a similar spatial structure as the original HSI. In this work, we revisit such vein from the respective of principal component analysis (PCA). Inspired by an alternative sparse PCA, we propose a spectral sparse subspace representation strategy to simultaneously learn low-dimensional spectral subspace and novel RCIs with sparse loadings. It turns out that the resulting RCIs possess a more significant spatial structure due to the adaptive sparse combination of spectral bands. A simple nonlocal low-rank approximation is then employed to further remove the residual noise of the RCIs. Finally, the entire denoised HSI is obtained by inverse spectral sparse PCA. Extensive experiments on the simulated and real HSI datasets show that the proposed nonlocal spectral sparse subspace representation method, dubbed as NS3R, has excellent performance both in denoising effect and running time compared with many other state-of-the-art methods.

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“…Almost all existing, either USL-or SSL-based, detection methods measure the abnormal level of each pixel using the reconstruction errors of the AE model. However, the reconstruction-based methods optimise the model by simply equivalent mapping between input and output data, which has the following problems: In the original spectral domain, the spectral information of HSI is affected by noise and spatial resolution to a certain extent [44,45], resulting in the insignificant separability of backgrounds and anomalies. Furthermore, the spectral dimension of HSI is higher.…”

Section: Introductionmentioning

confidence: 99%

Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection

Wang

Gao

et al. 2023

CAAI Trans on Intel Tech

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Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of discrimination between backgrounds and anomalies. This makes it easy for the autoencoder to capture the low‐level features shared between the two, thereby increasing the difficulty of separating anomalies from the backgrounds, which runs counter to the purpose of HAD. To this end, the authors map the original spectrums to the fractional Fourier domain (FrFD) and reformulate it as a mapping task in which restoration errors are employed to distinguish background and anomaly. This study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for HAD. Specifically, the depth separable features of backgrounds and anomalies are enhanced in the FrFD. Due to the semisupervised approach, FTSGAN needs to learn the embedded features of the backgrounds, thus mapping and restoring them from the FrFD to the original spectral domain. This strategy effectively prevents the model from focussing on the numerical equivalence of input and output, and restricts the ability of FTSGAN to restore anomalies. The comparison and analysis of the experiments verify that the proposed method is competitive.

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Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Cited by 19 publications

References 52 publications

Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection

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