Revolutionizing hyper spectral image denoising: a squeezenet paradigm.

Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification. Thanks to the powerful representation ability of untrained deep neural networks, deep image prior (DIP)-based methods achieve tremendous successes in image processing (e.g., denoising and inpainting). However, DIP-based methods neglect the tensor low-rank prior of the underlying HSI which will be beneficial to capturing the global structure of the underlying HSI. To address this issue, we propose a novel model for HSI denoising, which can simultaneously take respective advantages of the tensor low-rank prior and the deep spatial-spectral prior. The tensor low-rank prior leads to a better global structure and the deep spatial-spectral prior is complementary to preserve better local details. On the one hand, we adopt low-rank tensor ring (TR) decomposition to characterize the tensor low-rank prior and capture the global structure of the underlying HSI. On the other hand, we employ untrained deep neural networks to flexibly represent the deep spatial-spectral prior and capture the local details of the underlying HSI. To solve the proposed model, we develop an efficient alternating minimization algorithm. Experimental results on simulated and real data validate the advantages of the proposed model in HSI denoising. Compared with state-of-the-art HSI denoising methods, the proposed method preserves better local details and the global structure of the underlying HSI.

Hyperspectral image (HSI) denoising is an effective image processing technology to improve the quality of HSI. By exploiting the spectral-spatial characteristics of HSI, this paper proposes a HSI denoising method based on the Graph-structured Low Rank model and Non-Local constraint (GLRNL). Graph-structured low rank model can reveal the spectral similarity in HSI and preserve the distribution characteristics of spectral bands. The graph structure is used to ensure that the denoised HSI has similar spectral characteristics as the real HSI, and reduce the spectral distortions. Moreover, the non-local constraint in GLRNL adopts the spatial-spectral self-similarity and provides an effective side prior information for low rank calculation. Comparing with some traditional low rank and sparse model based HSI denoising methods, the proposed method achieves the competitive results on different simulated and real HSI data with various types of noise.

Recently, low-rank matrix recovery has been demonstrated to be an effective tool in hyperspectral images (HSIs) denoising. However, the previous low-rank matrix recovery method with a window of the fixed-shape cannot adaptively exploit spatial structure information and nonlocal similarity. In this paper, multiscale low-rank matrix recovery (MC-LRMR) is proposed to recover HSI corrupted by different kinds of noise. The proposed method contains three main steps. First, HSI is transformed by the principal component analysis (PCA) algorithm and multiscale superpixel segmentation is applied to the first principal component, to segment the HSI into non-overlapping homogeneous regions. Then, the mixed noises, including Gaussian noise, impulse noise, dead lines noise, stripes noise, are removed by the low-rank matrix recovery (LRMR) in a superpixel-by-superpixel manner. Finally, a fusion rule, i.e., average operator, is adopted to combine denoising results of various scales, to acquire a fused noise-free estimation. Experiments on simulated and real HSI data sets can demonstrate the effectiveness of the proposed method.

Recently, low-rank tensor recovery methods based on subspace representation have received increased attention in the field of hyperspectral image (HSI) denoising. Unfortunately, those methods usually analyze the prior structural information within different dimensions indiscriminately, ignoring the differences between modes, leaving substantial room for improvement. In this article, we first consider the low-rank properties in the subspace and prove that the structure correlation across the nonlocal self-similarity mode is much stronger than in the spatial sparsity and spectral correlation modes. On that basis, we introduce a new multidirectional low-rank regularization, in which each mode is assigned a different weight to characterize its contribution to estimating the tensor rank. After that, integrating the proposed regularization with the subspace-based tensor recovery framework, an optimization model for HSI mixed noise removal is developed. The proposed model can be addressed efficiently via the alternating minimization algorithm. Extensive experiments implemented with synthetic and real data demonstrate that the proposed method significantly outperforms other state-of-the-art HSI denoising methods, which clearly indicates the effectiveness of the proposed approach in HSI denoising.

Hyperspectral image denoising by total variation-regularized bilinear factorization

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore intrinsic similarity characteristics in both spatial dimension and spectral dimension, we conduct non-local spatial self-attention and global spectral self-attention with Transformer architecture. The window-based spatial self-attention focuses on the spatial similarity beyond the neighboring region. While, the spectral self-attention exploits the long-range dependencies between highly correlative bands. Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results. The code is released at https://github.com/MyuLi/SST.

Hyperspectral images (HSIs) have a wealth of applications in many areas, due to their fine spectral discrimination ability. However, in the practical imaging process, HSIs are often degraded by a mixture of various types of noise, for example, Gaussian noise, impulse noise, dead pixels, dead lines, and stripe noise. Low-rank matrix decomposition theory has been widely used in HSI denoising, and has achieved competitive results by modeling the impulse noise, dead pixels, dead lines, and stripe noise as sparse components. However, the existing low-rank-based methods for HSI denoising cannot completely remove stripe noise when the stripe noise is no longer sparse. In this article, we extend the HSI observation model and propose a double low-rank (DLR) matrix decomposition method for HSI denoising and destriping. By simultaneously exploring the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HSI, the two low-rank constraints are formulated into one unified framework, to achieve separation of the noise-free HSI, stripe noise, and other mixed noise. The proposed DLR model is then solved by the augmented Lagrange multiplier (ALM) algorithm efficiently. Both simulation and real HSI data experiments were carried out to verify the superiority of the proposed DLR method.

Sparse coding provides an excellent image prior for hyperspectral images (HSIs) denoising. However, on one hand, it is challenging to capture the structure within each sparse code for improving the reconstruction accuracy, on the other hand, the inconsistent recovery of the sparse codes corrupts the spectrum similarity in each homogeneous cluster of the HSI. To address these problems, we first propose a novel covariance matrix estimation based structured sparse coding method, where the sparse code matrix is modeled by a matrix normal distribution with a full covariance matrix. By estimating the covariance matrix with a latent variable based Bayesian framework, the data-dependent and noise-robust structure for each sparse code is learned from the noisy observation, with which the sparse codes are reconstructed accurately. Then, an intra-cluster filtering is employed to restore the spectrum similarity in each cluster. Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods in HSIs denoising.

A significant challenge in hyperspectral remote sensing image analysis is the presence of noise, which has a negative impact on various data analysis methods such as image classification, target detection, unmixing etc. In order to address this issue, hyperspectral image denoising is used as a preprocessing step prior to classification. This paper presents an effective, fast and reliable method for denoising hyperspectral images followed by classification based on sparse representation of hyperspectral data. The use of Legendre-Fenchel transform for denoising is an effective spatial preprocessing step to improve the classification accuracy. The main advantage of Legendre-Fenchel transform is that it removes the noise in the image while preserving the sharp edges. The sparsity based algorithm namely, Orthogonal Matching Pursuit (OMP) is used for classification. The experiment is done on Indian Pines data set acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. It is inferred that the denoising of hyperspectral images before classification improves the Overall Accuracy of classification. The effect of preprocessing using Legendre Fenchel transformation is shown by comparing the classification results with Total Variation (TV) based denoising. A statistical comparison of the accuracies obtained on standard hyperspectral data before and after denoising is also analysed to show the effectiveness of the proposed method. The experimental result analysis shows that for 10\(\%\) training set the proposed method leads to the improvement in Overall Accuracy from 83.18\(\%\) to 91.06\(\%\), Average Accuracy from 86.17\(\%\) to 92.78\(\%\) and Kappa coefficient from 0.8079 to 0.8981.

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method.

Target detection is a key issue in processing hyperspectral images (HSIs). Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. In most cases, both the target spatial distributions and the spectral signatures are unknown, so each pixel is separately tested and appears as a target when it significantly differs from the background. In this paper, we propose two algorithms to improve the signal-to-noise ratio (SNR) of hyperspectral data, leading to detectors that are robust to noise. These algorithms consist in integrating adaptive spatial/spectral filtering into the adaptive matched filter and adaptive coherence estimator. Considering the HSIs as tensor data, our approach introduces a data representation involving multidimensional processing. It combines the advantages of spatial and spectral information using an alternating least square algorithm. To estimate the signal subspace dimension in each spatial mode, we extend the Akaike information criterion, and we develop an iterative algorithm for spectral-mode rank estimation. We demonstrate the interest of integrating the quadtree decomposition to perform an adaptive 3-D filtering and thereby preserve the local image characteristics. This leads to a significant improvement in terms of denoised tensor SNR and, consequently, in terms of detection probability. The performance of our method is exemplified using simulated and real-world HYperspectral Digital Imagery Collection Experiment images.

Generally, the improvement in resolution will lead to larger data volume and higher data dimension for Hyperspectral image, which raise a higher requirement for previous image processing algorithms. In this paper, a novel coupled spectral-spatial tensor representation framework (CSSTR) is proposed for denoising of hyperspectral images. Specifically, the proposed method is applied to describe the spectral-spatial features which decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a matrix and a vector. Owing to the spatial-spectral constraint fed back from the tensor representation method, CSSTR can capture the structural correlations and inherent feature information of data. Finally, several experiments were conducted to illustrate the advantage of the proposed algorithm.

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often suffer from degradation such as noise and low resolution. In this paper, we propose an effective model for hyperspectral image (HSI) restoration, specifically image denoising and super-resolution. Our model considers three underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the degraded HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI restoration model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. In experiments on HSI denoising, we show that the proposed method outperforms many state-of-the-art methods under several comprehensive quantitative assessments. We also show that our method performs well on HSI super-resolution.

Bayesian deep matrix factorization network for multiple images denoising

The degradation of spaceborne hyperspectral images (HSIs) usually results from various types of noise. In this letter, we propose a 3D hybrid higher degree total variation regularized nonconvex local low-rank tensor recovery (H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV-NLRTR) model to restore the HSIs. Inspired by the good performance of the higher degree total variation penalty in image denoising, we first develop a 3D hybrid higher degree total variation penalty term, which is able to capture the fine image details and edges along the spatial dimensions and spectral dimension. The tensor multi-Schatten- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> norm is chosen as the relaxation of the low-rank tensor constraint, which can not only separate the low-rank clean HSI patches from noisy images effectively but also improve the computational efficiency. The proposed H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV-NLRTR model can simultaneously characterize the spectral correlation and the spatial structure of the HSI dataset by incorporating the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV penalty in the nonconvex local low-rank tensor recovery problem. In addition, we adopt a fast iterative majorize-minimize algorithm to efficiently solve the corresponding optimization problem. The numerical experiments on both simulated and real HSI datasets demonstrate that the proposed algorithm provides consistently improved restoration results compared with the state-of-the-art algorithms.