Remote Sensing Image Denoising Research Articles

Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising

Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising

A differential evolution with autonomous strategy selection and its application in remote sensing image denoising

Differential evolution (DE) is an efficient and effective global optimization algorithm, and many DE variants with adaptive strategy selection have been proposed. However, among the existing DE variants with adaptive strategy selection, only a few last generations of population information are utilized to conduct statistical analysis for strategy selection, and the population cumulative historical experience information does not be fully used to assist population to effectively search. In this paper, a DE with autonomous strategy selection (ASS-DE) is proposed to make full use of the population cumulative historical experience information. In ASS-DE, an individual can autonomously choose the better mutation strategy guided by the cumulative historical experience. As the same time, a parameter updating mechanism with archive is introduced to assign appropriate control parameters to the strategy. Additionally, an evolutionary learning mechanism based on individual similarity is proposed to guide the learning of historical experience information. To verify the performance of ASS-DE, CEC2015 and CEC2017 benchmark functions and the real-world optimization problem of wavelet parameter optimization in remote sensing image denoising is compared with some state-of-the-art intelligence algorithms. The experimental results show that ASS-DE obtains the promising performance compared with other DE variants. The source code of ASS-DE can be found in GitHub at https://github.com/Ramessis/ASS-DE_denoising.git.

Application of Multiple-Optimization Filtering Algorithm in Remote Sensing Image Denoising.

Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics.

A Second-Order Method for Removing Mixed Noise from Remote Sensing Images.

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.

Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

Remote sensing images are widely applied in instance segmentation and objetive recognition; however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed a novel model for remote sensing image denoising, called the anisotropic weighted total variation feature fusion network (AWTVF2Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTVF2Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, PatternNet, UCL), and the experimental results show that AWTVF2Net can obtain 0.12∼19.39 dB/0.0237∼0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLIINDS-II, and BRISQUE values of AWTVF2Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94∼12.92 higher, 8.33∼27.5 higher, and 2.2∼5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images.

Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”

The motivations behind this Special Issue, announced in 18 August 2020, were the following [...]

Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

Remote sensing image denoising based on improved semi-soft threshold

Remote sensing image denoising has important applications in aerospace, geophysical exploration and communication engineering. Traditional wavelet transform cannot represent the details and contours of the image texture effectively, because pseudo-Gibbs effect, ringing and blurring of edge details will be produced during de-noising. In this paper, in view of the shortcomings of the existing directional filter design, an eight-direction filter bank that directly decomposes each direction of the image using each directional subband filter is proposed, which is combined with Laplace pyramid transformation to form an optimized contour transformation. A denoising threshold processing algorithm for remote sensing images based on optimized contourlet transformation is proposed. Compared with the traditional denoising method, the improved algorithm has the characteristics of multi-scale and multi-direction, and can better capture the detailed information of the image. The improved semi-soft threshold function is used to process the transformed coefficients, which can better restore the edge information in the image. The results of a series of simulation experiments show that compared with the traditional wavelet threshold function, the contourlet hard threshold function and contourlet soft threshold function, this method achieves better visual effects and higher PSNR (peak signal to noise ratio) while the image is denoised, and the denoising effect is improved by 0.11%. The proposed new threshold function is feasible and can handle the texture information and edge information of remote sensing image well.

RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

Joint Analysis and Weighted Synthesis Sparsity Priors for Simultaneous Denoising and Destriping Optical Remote Sensing Images

Stripe and random noise are two different degradation phenomena that commonly coexist in optical remote sensing images, and they are often modeled as inverse problems. In model-based inverse problems, analysis and synthesis sparse representations (SSRs) are used as regularization terms to obtain approximate solutions due to their respective merits, i.e., the nonzero coefficients in SSR are usually used to describe an image, while the indexes of zeros in analysis sparse representation (ASR) are used to characterize the stripe. Inspired by these merits, we propose a unified variational framework, called a joint analysis and weighted synthesis (JAWS) sparsity model, to simultaneously separate the clean image and the stripe from a single optical remote sensing image. To solve the JAWS sparsity model efficiently, an alternating minimization optimization strategy is first employed to separate it into two subproblems that are used for different tasks. One called as weighted SSR (WSSR) is the main for optical remote sensing image denoising, which can be effectively solved by employing the weighted singular value thresholding operator, while the other called as ASR is the main approach for optical remote sensing image destriping, which is optimized by adopting the split Bregman iteration. By minimizing the two subproblems alternatively, the proposed JAWS sparsity model is efficiently solved. Finally, both quantitative and qualitative results of experiments on synthetic and real-world optical remote sensing images validate that the proposed approach is effective and even better than the state of the arts.

ANALYSIS OF DENOISING INTERPRETATION OF REMOTE SENSING IMAGE BASED ON ICA-WAVELET TRANSFORM

Abstract. The noise will blur the key information of the remote sensing image, such as edge texture and important feature information, which will result in the loss of key information contained in the remote sensing image, resulting in the degradation of the overall quality of the image, which will bring difficulties to the interpretation work. Therefore, in order to obtain higher precision, signal-to-noise ratio and improve the quality of remote sensing image, denoising the remote sensing image containing noise is a crucial step and processing step for image remote sensing image application.In this paper, the ICA wavelet analysis algorithm is applied to the application of real-time remote sensing image denoising. A series of pre-processing procedures such as control point correction, image fusion and image mosaic are carried out on the Asian sub-level remote sensing image, and the signal-to-noise ratio of the remote sensing image is adopted. (SNR/dB) and mean square error (RMSE) verify the image quality after denoising.

Performance Analysis of Signal Denoising and Remote Sensing Image Denoising in Photoelectric Wireless Sensor Networks Based on Matched Wavelet

Performance Analysis of Signal Denoising and Remote Sensing Image Denoising in Photoelectric Wireless Sensor Networks Based on Matched Wavelet

The NSCT-HMT Model of Remote Sensing Image Based on Gaussian-Cauchy Mixture Distribution

The nonsubsampled Contourlet transform (NSCT) not only retains the characteristics of Contourlet transform, but also has the good characteristic of shift-invariance, which plays a significant role in denoising, fusion, and segmentation of texture-rich images. The NSCT not only retains the properties of Contourlet transform, but also has the important property of shift-invariance, which plays a significant role in image processing, such as denoising, fusion, and segmentation of texture-rich images. This paper proposes a Gaussian-Cauchy mixture distribution-based NSCT hidden Markov tree model (GC-NSCT-HMT). The specific form of Gaussian-Cauchy mixture distribution is determined by the kurtosis of the NSCT coefficients in each subband. First, we study the probability density distribution of the remote sensing image NSCT coefficients and then propose the Gaussian-Cauchy mixture distribution, which can adaptively adjust according to the statistical property of NSCT coefficients through a balance function. Experimental results show that the proposed mixture distribution can achieve a good imitative effect to the NSCT coefficients. Second, we study the marginal statistical property and the joint statistical property of the NSCT coefficients, the persistence and aggregation properties of them are also studied in depth. We find that the ‘father’ NSCT coefficient can transfers to its son coefficients through a tree structure. Third, we combine the above conclusions with the hidden Markov tree model (HMT) and the GC-NSCT-HMT model is proposed. Finally, we apply our model to remote sensing image denoising. The subjective and objective experimental results demonstrate the feasibility of the proposed method.

A Parallel Nonlocal Means Algorithm for Remote Sensing Image Denoising on an Intel Xeon Phi Platform

The nonlocal means (NLM) algorithm is one of the best image denoising algorithms because of its superior capability to retain the texture details of an image and is widely used in remote sensing (RS) image preprocessing. However, the time complexity of the algorithm is very high due to its nonlocality when searching for similar pixels. As a result, the NLM algorithm cannot satisfy the near real-time requirements of some specific applications. To resolve this issue, a parallel NLM algorithm based on Intel Xeon Phi hardware with Intel’s many integrated cores (MIC) architecture was designed and implemented in this paper. The parallel algorithm achieved satisfactory speedup, but the speedup obtained showed a step-like distribution for different image sizes. This result was not expected based on the theoretical analysis, which predicted that the speedup should be independent of input data set size. To address this problem, the parallel algorithm was further optimized by adding pretreatment approaches and cutting down the number of nested loops in the MIC. Finally, experiments using the standard and optimized versions were carried out using the RS images of different sizes. Several conclusions could be drawn from the experimental results: 1) the standard parallel algorithm can obtain better speedup with only one MIC card and 2) the optimized parallel algorithm can completely eliminate the step distribution of the speedup and can also accelerate RS image processing significantly.

Regularized Least Square Approach for Remote Sensing Image Denoising using Wavelet Filters

Noise in remote sensing images (aerial and satellite) is caused due to various reasons such as atmospheric interference or lack of quality in sensors used to capture them. Removal of noise in an efficient way is a big challenge for researchers. In this paper, one dimensional signal denoising based on weighted regularized least square method is mapped to two dimensional image de noising. Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The proposed technique for image denoising based on Least Square (LS) approach is experimented on five different satellite and aerial images corrupted by gaussian noise with varying noise levels and regularization parameter lambda (λ) for different wavelet filter coefficients such as ‘haar’, ‘symlet’, ‘daubechies’ and coiflet. The effectiveness of the proposed method of image denoising is compared against the existing second order filter [based on LS] and conventional wavelet based image denoising technique based on the standard metric called Peak Signal to Noise Ratio (PSNR). Findings: From the experimental result analysis obtained it is inferred that the wavelet filters outperforms the second order filter and the conventional wavelet based image denoising. The complexity of the mathematics is low in our proposed method for image denoising. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

Remote Sensing Image De-noising via Sparse Representation based on Non-local Self-similarity

Remote Sensing Image De-noising via Sparse Representation based on Non-local Self-similarity

Hyperspectral Remote Sensing Image Denoising Based on Non-Local Low-Rank Dictionary Learning

For hyperspectral remote sensing image denoising, this paper proposed image denoising based on non-local low-rank dictionary learning. The basic idea of algorithm is to use strong relativity of all wave bands of hyperspectral remote sensing image with local self-similarity and local sparsity of image to improve the denoising performance. First of all, combined with the strong relativity, non-local self-similarity and local sparsity, non-local low-rank dictionary learning is established. Then iterative method is used to solve the model to get redundant dictionary and sparsity to represent coefficient. Finally, redundant dictionary and sparsity is used to express restored image of coefficient. Compared with the existing advanced algorithm, by making full use of strong relativity each band of hyperspectral image, it makes the algorithm obtain the information on details to well keep the hyperspectral remote sensing image, to improve the visual effect. Experimental results verify the effectiveness of the algorithm in this paper.

Towards a partial differential equation remote sensing image method based on adaptive degradation diffusion parameter

For the anisotropy diffusion feature, Partial Differential Equation (PDE) methods keep edge detail characters well in case of denoising, thus being widely applied in remote sense image denoising, smoothing, filtering and reconstruction. A PDE remote sensing image denoising method based on Adaptive Degradation Diffusion Parameter (ADDP) was proposed in the paper to deal with fuzzy detail problem caused by increasing iteration number. The PDE denoising method with ADDP enlarged diffusion size in the plat region of remote sensing image without affecting the remote sensing image edge, thus avoiding loss of remote sensing image detail and intersections caused by Gaussian convolution smoothing in the PDE filtering model based on curvature-based movement (CM) and image denoising model based on total variation (TV). In the region where gradation value changes little, the method executed isotropic diffusion to remove isolated noise. The upwind scheme was applied for model numerical realization. Experimental in remote sensing image denoising results proved its feasibility and effectiveness.