scholarly journals Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

2021 ◽  
Vol 13 (16) ◽  
pp. 3196
Author(s):  
Wei Liu ◽  
Chengxun He ◽  
Le Sun
Keyword(s):  
Coefficient Matrix ◽  
Noise Removal ◽  
Block Matching ◽  
Low Rank ◽  
Self Similarity ◽  
Basis Matrix ◽  
Block Matrices ◽  
Non Local ◽  
Mixed Noise ◽  
Local Block

During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.

scholarly journals Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 158
Author(s):  
Jiucheng Xu ◽  
Yihao Cheng ◽  
Yuanyuan Ma
Keyword(s):  
Image Denoising ◽  
Block Matching ◽  
Low Rank ◽  
Self Similarity ◽  
Data Set ◽  
Noisy Images ◽  
Norm Minimization ◽  
Rank Matrix ◽  
Non Local ◽  
Low Rank Matrix

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.

Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

2017 ◽  
Vol 26 (7) ◽  
pp. 3171-3186 ◽  
Author(s):  
Tao Huang ◽  
Weisheng Dong ◽  
Xuemei Xie ◽  
Guangming Shi ◽  
Xiang Bai
Keyword(s):  
Noise Removal ◽  
Mixture Modeling ◽  
Low Rank ◽  
Scale Mixture ◽  
Low Rank Approximation ◽  
Mixed Noise ◽  
Rank Approximation ◽  
Mixed Noise Removal

scholarly journals SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

2020 ◽  
Vol 12 (18) ◽  
pp. 2979
Author(s):  
Le Sun ◽  
Chengxun He ◽  
Yuhui Zheng ◽  
Songze Tang
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Subspace ◽  
Low Rank ◽  
Spatial Correlations ◽  
Self Similarity ◽  
Multiple Datasets ◽  
Alternating Direction ◽  
Non Local ◽  
Low Dimensional ◽  
Spatial Redundancy

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.

scholarly journals Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

2019 ◽  
Vol 2019 ◽  
pp. 1-23
Author(s):  
Kriengkri Langampol ◽  
Kanabadee Srisomboon ◽  
Vorapoj Patanavijit ◽  
Wilaiporn Lee
Keyword(s):  
Communication Channel ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Block Matching ◽  
Trilateral Filter ◽  
Optimal Value ◽  
Spatial Weight ◽  
Mixed Noise ◽  
Mixed Noise Removal ◽  
Noise Characterization

Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

Low-rank tensor completion via combined non-local self-similarity and low-rank regularization

2019 ◽  
Vol 367 ◽  
pp. 1-12 ◽  
Author(s):  
Xiao-Tong Li ◽  
Xi-Le Zhao ◽  
Tai-Xiang Jiang ◽  
Yu-Bang Zheng ◽  
Teng-Yu Ji ◽  
...  
Keyword(s):  
Low Rank ◽  
Tensor Completion ◽  
Self Similarity ◽  
Rank Tensor ◽  
Non Local

Mixed noise removal by weighted low rank model

2015 ◽  
Vol 151 ◽  
pp. 817-826 ◽  
Author(s):  
Jielin Jiang ◽  
Jian Yang ◽  
Yan Cui ◽  
Lei Luo
Keyword(s):  
Noise Removal ◽  
Low Rank ◽  
Mixed Noise ◽  
Mixed Noise Removal

scholarly journals An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

2020 ◽  
Vol 12 (16) ◽  
pp. 2582
Author(s):  
Yanan You ◽  
Rui Wang ◽  
Wenli Zhou
Keyword(s):  
Synthetic Aperture Radar ◽  
Urban Area ◽  
Tensor Decomposition ◽  
Block Matching ◽  
Low Rank ◽  
Synthetic Aperture ◽  
Tensor Model ◽  
Cp Decomposition ◽  
Non Local ◽  
Aperture Radar

The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio.

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