Stripe Noise Research Articles

Effect of scattered megavoltage x-rays on markerless tumor tracking using dual energy kilovoltage imaging.

To determine the effect of megavoltage (MV) scatter on the accuracy of markerless tumor tracking (MTT) for lung tumors using dual energy (DE) imaging and to consider a post-processing technique to mitigate the effects of MV scatter on DE-MTT. A Varian TrueBeam linac was used to acquire a series of interleaved 60/120kVp images of a motion phantom with simulated tumors (10 and 15 mm diameter). Two sets of consecutive high/low energy projections were acquired, with and without MV beam delivery. The MV field sizes (FS) ranged from 2×2cm2 -6×6cm2 in steps of 1×1cm2 . Weighted logarithmic subtraction was performed on sequential images to produce soft-tissue images for kV only (DEkV ) and kV with MV beam on (DEkV+MV ). Wavelet and fast Fourier transformation filtering (wavelet-FFT) was used to remove stripe noise introduced by MV scatter in the DE images ( ). A template-based matching algorithm was then used to track the target on DEkV, DEkV+MV , and images. Tracking accuracy was evaluated using the tracking success rate (TSR) and mean absolute error (MAE). For the 10 and 15 mm targets, the TSR for DEkV images was 98.7% and 100%, and MAE was 0.53 and 0.42mm, respectively. For the 10mm target, the TSR, including the effects of MV scatter, ranged from 86.5% (2×2cm2 ) to 69.4% (6×6cm2 ), while the MAE ranged from 2.05mm to 4.04mm. The application of wavelet-FFT algorithm to remove stripe noise ( ) resulted in TSR values of 96.9% (2×2cm2 ) to 93.4% (6×6cm2 ) and subsequent MAE values were 0.89mm to 1.37mm. Similar trends were observed for the 15mm target. MV scatter significantly impacts the tracking accuracy of lung tumors using DE images. Wavelet-FFT filtering can improve the accuracy of DE-MTT during treatment.

Remote sensing image destriping based on variable weight and group sparse regularization

Striping effects are common phenomena in remote sensing images, and they significantly limit subsequent applications. Although many destriping approaches have been developed, there aren’t many that can completely eliminate complex stripes with varying levels of strength. To address this issue, we propose a stripe removal model based on a variable weight coefficient and group sparse regularization. Specifically, rather than a single scalar for the stripes in most approaches, different weights are set for different stripe rows to estimate the stripes with varying intensities. An adaptive method to estimate the weight matrix is proposed. On the other hand, group sparsity regularization is employed to constrain the entire stripe. In addition, region weights are designed for regions with different stripe characteristics. The alternating direction multiplier method is employed to solve the proposed model by alternating minimization. Experimental results based on simulation and real data demonstrate that the proposed model outperforms other advanced methods in terms of stripe noise removal and image detail preservation.

Comparative Analysis of Striping Noise between FY-3E MWTS-3 and FY-3D MWTS-2

On 5 July 2021, China launched the world’s first early morning orbit meteorological satellite, which was equipped with China’s newest-generation microwave instrument, the Microwave Temperature Sounder-3 (MWTS-3). MWTS-3 has 17 detection channels, which can detect temperature profiles from near the ground to approximately 2 hPa. However, similar to the newest-generation microwave temperature instruments in other countries, MWTS-3 may also suffer from striping noise. In this paper, a principal component analysis (PCA) is combined with ensemble empirical mode decomposition (EEMD) to extract the striping noise from the brightness temperatures observed by MWTS-3, and a comparative analysis is performed with its predecessor (MWTS-2). This paper analyzes the characteristics of the striping noise of both instruments and its possible impact on their observations. The results show that the striping noise of MWTS-3 has been significantly reduced in most of the 17 channels. In particular, the striping noise of MWTS-3’s channel 3 is reduced by nearly half compared to the channel of MWTS-2 with the same frequency. Furthermore, the interchannel correlations for MWTS-3 are significantly lower than those for MWTS-2. After noise mitigation, the interchannel correlations of both MWTS-2 and MWTS-3 are obviously reduced. In addition, striping noise mitigation can reduce observation errors to some extent, especially for channels above the middle troposphere.

Infrared image stripe noise removal using wavelet analysis and parameter estimation

Infrared image systems often generate stripe noise because of the non-uniformity of the focal plane array, which significantly reduces the visual quality of the image. To solve this problem, we proposed an effective single-frame denoising method in this paper. First, the wavelet function extracts the approximate and vertical components of the original image containing stripe noise. Then, the approximation component is denoised by parameter estimation, and the vertical component is denoised by guided filtering. Finally, wavelet reconstruction is performed to realize the denoising process of the original image. This method avoids the loss of details of other components of the image and achieves an excellent denoising effect. The experimental results on the public datasets indicate that our proposed method can effectively eliminate the stripe noise of infrared images compared with some advanced methods.

A Bispectral Approach for Destriping and Denoising the Sea Surface Temperature from SGLI Thermal Infrared Data

Abstract Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement.

A two-stage framework for optical coherence tomography angiography image quality improvement.

Optical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis. In this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement. To evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities.

D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images

Mixed (random and stripe) noise will cause serious degradation of optical remotely sensed image quality, making it hard to analyze their contents. In order to remove such noise, various inverse problems are usually constructed with different priors, which can be solved by either model-based optimization methods or discriminative learning methods. However, they have their own drawbacks, such as the former methods are flexible but are time-consuming for the pursuit of good performance; while the later methods are fast but are limited for extensive applications due to their specialized tasks. To fast obtain pleasing results with combination of their merits, in this paper, we propose a novel denoising strategy, namely, Dual Denoiser Driven Convolutional Neural Networks (D3CNNs), to remove both random and stripe noise. The D3CNNs includes the following two key parts: one is that two auxiliary variables respective for the denoised image and the stripe noise are introduced to reformulate the inverse problem as a constrained optimization problem, which can be iteratively solved by employing the alternating direction method of multipliers (ADMM). The other is that the U-shape network is used for the denoised auxiliary variable while the residual CNN (RCNN) for the stripe auxiliary variable. The subjectively and objectively comparable results of experiments on both synthetic and real-world remotely sensed images verify that the proposed method is effective and is even better than the state-of-the-arts.

On-Orbit Relative Radiometric Calibration of the Bayer Pattern Push-Broom Sensor for Zhuhai-1 Video Satellites

The two video satellites of the second and third batch of Zhuhai-1 microsatellites (referred to as OVS-2A/3A) are operational with their hyperspectral satellites, which improves the data acquisi-tion capability of the Zhuhai-1 remote sensing satellite constellation. Contrary to the linear array push-broom hyperspectral satellites and plane array CCD video satellites, the OVS satellite is equipped with a planar array Bayer pattern sensor, which can obtain single-band grayscale images by push-broom imaging. Additionally, the Bayer color reconstruction algorithm can interpolate sensor data to provide RGB color band information. Therefore, for the Bayer pattern push-broom sensor, the relative calibration method of linear push-broom or array cameras cannot be directly applied. The radiometric calibration of the Bayer pattern push-broom imaging mode has become a matter of concern; therefore, this study developed a radiometric calibration method for the Bayer pattern push-broom sensor of the OVS satellite and verified its effectiveness and accuracy. OVS images were used to perform on-orbit relative radiometric calibration, and the calibration accu-racy, including streaking metrics and root-mean-square error, was better than 1%, meeting the specification requirements for the OVS satellite. Visually, after calibration correction, the streaking and striping noise of the Bayer images was removed, and the radiometric quality of the image was considerably improved, providing a good data basis for subsequent research in remote sensing applications.

Fast Hyperspectral Image Denoising and Destriping Method Based on Graph Laplacian Regularization

Hyperspectral images (HSIs) contain rich spatial and spectral information about the earth, and are widely used in the remote sensing field. However, an HSI is frequently corrupted by various types of noise, such as Gaussian noise, sparse noise, stripe noise, and so on, which severely limits the subsequent application of the HSI. In this paper, we propose a graph Laplacian regularizer (GLR) to exploit the low-rank information across the bands of the HSI. Compared with the traditional low-rank regularization, our graph Laplacian regularization can achieve equivalent or better performance with less time consumption. Besides, a sparse constraint and a low-rank constraint are employed to remove the sparse and stripe noise. In addition, the augmented Lagrangian multiplier is used to solve each component to restore a clean image. Finally, we have carried out experiments on the simulated and real noisy HSI data. The results show the superiority of the proposed method over state-of-the-art methods, in terms of PSNR and SSIM, time cost and visual effect. The MATLAB code is available through: https://github.com/zzhang-99/FGLR.

Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity

In hyperspectral images (HSIs), mixed noise ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., Gaussian noise, impulse noise, stripe noise, and deadlines) contamination is a common phenomenon that greatly reduces the visual quality of the image. In recent years, methods combining global and non-local low-rankness have been widely used in the field of HSI denoising. However, most methods apply original space-based denoising strategies (low-rank tensor decomposition, total variation, and tensor sparse representation, etc.) directly to the modeling of non-local low-rank tensors in subspace, without fully exploiting the intrinsic and latent properties of the non-local similar tensors. In this paper, we propose a hybrid prior denoising method based on global tensor low-rankness and non-local SVD-aided group sparsity (GTL_NSGS). This method introduces a novel plug-and-play NSGS denoiser that uses singular value decomposition as assistance to successively explore self-similarity of spatial dimension, low-rankness of spectral dimension, and group sparsity of difference domain in subspace non-local similar tensors. Globally, we utilize the existing three-way log-based tensor nuclear norm (3DLogTNN) to approximate the HSI tensor fibered rank and introduce a difference continuity regularization to obtain a continuous smooth spectral basis. Finally, we combine the Alternating Direction Method of Multipliers (ADMM) with the Augmented Lagrangian Multiplication (ALM) algorithm to solve the proposed model effectively. Extensive experiments on simulated and real data sets demonstrate that the proposed method has superior performance in removing mixed noise compared to state-of-the-art denoisers.

Progressive Task-Based Universal Network for Raw Infrared Remote Sensing Imagery Ship Detection

Infrared remote sensing images are becoming increasingly popular due to their superior penetration and resistance to light interference. However, challenges still remain when applying them in real-world applications: 1) raw infrared images suffer from severe stripes interference, and the preprocessing techniques used to obtain standard image products for subsequent detection tasks tend to be time-consuming, which fails to meet the application requirements; 2) current destriping techniques may inevitably weaken the local contrast between some objects and the local background since they need to consider the gray consistency of the overall image; 3) in low-resolution images, dim and small infrared targets are challenging to discriminate, resulting in high false alarms. To address these challenges, we proposed a progressive task-based universal network for raw infrared image ship detection while simultaneously removing stripes. First, we built an integrated network consisting of two components: the stripe denoising component (SDC) and the object detection component (ODC). We also designed a feedback loss adjustment mechanism to enhance the focus of the SDC on the target area. Second, a directed two-branch network was constructed for efficient stripe noise removal, including an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> -direction branch for feature enhancement and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> -direction branch for grayscale smoothing. Finally, a parallel network with two labels was designed to extract the inherent features of the target and the background, as well as their relationship features, to achieve refined ship detection. We conducted experiments on a self-assembled dataset from the GaoFen-1 satellite to validate our approach. The experimental results demonstrated that the proposed method outperformed other state-of-the-art methods in infrared image ship detection.

Leveraging “Night–Day” Calibration Data to Correct Stripe Noise and Vignetting in SDGSAT-1 Nighttime-Light Images

Leveraging “Night–Day” Calibration Data to Correct Stripe Noise and Vignetting in SDGSAT-1 Nighttime-Light Images

CPAD: Component Pixel-Aware Multispectral Image Destriping

Stripe noise removal of Multispectral images (MSIs) is a challenging topic and has attracted substantial research attention in remote sensing areas. Existing destriping methods mainly concentrate on patch-based model representation, which ignores the heterogeneous components across the degraded MSIs and poorly utilizes the prior knowledge of the image cubes of the MSIs on high-level perception. In this paper, we present a component pixel-aware MSIs destriping method. Intuitively, the image cube of the MSIs is divided into three different components (the jump, transition, and gentle components) based on the gradients of each pixel. Then, three different regularization operators are adopted adaptively for different components in a pixel-level manner. In addition, to properly depict the stripe cube of the MSIs, we exploit the non-convex non-smooth iteratively reweight nuclear norm. By incorporating both improved terms, our proposed method can estimate the component-variant linear representation coefficients and model the structural information of both the stripe cube and image cube. The alternating direction method of multipliers (ADMM) is adopted to solve the proposed method. Evaluating various destriping performance measurements, the algorithm proposed in this paper outperforms other state-of-the-art methods under extensive experiments on synthetic and real datasets.

A Novel Two-Stage Destriping Algorithm Based on MWIR Energy Separation and Image Guidance (MES-IG)

Long-wave infrared (LWIR) bands in multispectral datasets are extremely useful in many applications. However, the LWIR bands usually suffer from undesirable stripe noise, which impedes their further application. Compared with emission-dominated LWIR, the mid-wave infrared (MWIR) bands containing both emitted and reflected radiation usually exhibit higher image quality. In this paper, we propose a novel two-stage MWIR energy separation and image guidance (MES-IG) algorithm to destripe the LWIR images with the assistance of the MWIR bands. In the first stage, we decompose the MWIR image into the emitted and reflected components by solving a constrained optimization problem. Specifically, we impose the low-rank penalty to enforce the similarities between MWIR and LWIR, and we use the total variation (TV) regularization to exploit the similarities between MWIR and visible and near-infrared (VNIR) images. In the second stage, the obtained emitted component of MWIR is considered as the guidance image to remove the stripes in the LWIR images by adopting the 1-D guided filter algorithm. Numerical experiments on Chinese Gaofen-5 satellite and MODIS data demonstrate the utility of the proposed method in providing improved LWIR image destriping performance over the state-of-the-art algorithms.

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

Simultaneous Destriping and Image Denoising using a Nonparametric Model with the EM Algorithm.

Digital images often suffer from the common problem of stripe noise due to the inconsistent bias of each column. The existence of the stripe poses much more difficulties on image denoising since it requires another n parameters, where n is the width of the image, to characterize the total interference of the observed image. This paper proposes a novel EM-based framework for simultaneous stripe estimation and image denoising. The great benefit of the proposed framework is that it splits the overall destriping and denoising problem into two independent sub-problems, i.e., calculating the conditional expectation of the true image given the observation and the estimated stripe from the last round of iteration, and estimating the column means of the residual image, such that a Maximum Likelihood Estimation (MLE) is guaranteed and it does not require any explicit parametric modeling of image priors. The calculation of the conditional expectation is the key, here we choose a modified Non-Local Means algorithm to calculate the conditional expectation because it has been proven to be a consistent estimator under some conditions. Besides, if we relax the consistency requirement, the conditional expectation could be interpreted as a general image denoiser. Therefore other state-of-the-art image denoising algorithms have the potentials to be incorporated into the proposed framework. Extensive experiments have demonstrated the superior performance of the proposed algorithm and provide some promising results that motivate future research on the EM-based destriping and denoising framework.

Dual Self-Attention Swin Transformer for Hyperspectral Image Super-Resolution

Spatial resolution is a crucial indicator for measuring the quality of hyperspectral imaging (HSI) and obtaining high-resolution (HR) hyperspectral images without any auxiliary information has become increasingly challenging. One promising approach is to use deep-learning (DL) techniques to reconstruct HR hyperspectral images from low-resolution (LR) images, namely super-resolution (SR). While convolutional neural networks are commonly used for hyperspectral image SR (HSI-SR), they often lead to unavoidable performance degradation due to the lack of long-range dependence learning ability. In this article, we propose a dual self-attention Swin transformer SR (DSSTSR) network that utilizes the ability of the shifted windows (Swin) transformer in the spatial representation of both global and local features and learns spectral sequence information from adjacent bands of HSI. Additionally, DSSTSR incorporates an image denoising module using the wavelet transformation method to mitigate the impact of stripe noise on HSI-SR. Our extensive experiments using publicly close-range datasets demonstrate that DSSTSR outperforms other state-of-art HSI-SR methods in terms of three image quality metrics. Furthermore, we applied DSSTSR to the SR of satellite hyperspectral images and achieved improved classification results. Compared to its competitors, DSSTSR exhibits superior performance in enhancing spatial resolution while preserving spectral information. These results suggest that the DSSTSR network has great potential for standardization in remote-sensing image processing and practical applications.

Stripe noise removal in high resolution satellite remote sensing images based on deep learning

Stripe noise removal in high resolution satellite remote sensing images based on deep learning

A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet

ZY1-02D is a Chinese hyperspectral satellite, which is equipped with a visible near-infrared multispectral camera and a hyperspectral camera. Its data are widely used in soil quality assessment, mineral mapping, water quality assessment, etc. However, due to the limitations of CCD design, the swath of hyperspectral data is relatively smaller than multispectral data. In addition, stripe noise and collages exist in hyperspectral data. With the contamination brought by clouds appearing in the scene, the availability is further affected. In order to solve these problems, this article used a swath reconstruction method of a spectral-resolution-enhancement method using ResNet (SRE-ResNet), which is to use wide swath multispectral data to reconstruct hyperspectral data through modeling mappings between the two. Experiments show that the method (1) can effectively reconstruct wide swaths of hyperspectral data, (2) can remove noise existing in the hyperspectral data, and (3) is resistant to registration error. Comparison experiments also show that SRE-ResNet outperforms existing fusion methods in both accuracy and time efficiency; thus, the method is suitable for practical application.

Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image

ABSTRACT Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R 2, RMSE, rRMSE and skewness in most bands ). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed.