Noise Level Estimation Research Articles

Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images.

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise-power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.

Gravitational interaction of ultralight dark matter with interferometers

Ultralight dark matter exhibits an order-one density fluctuation over the spatial scale of its wavelength. These fluctuations gravitationally interact with gravitational wave interferometers, leading to distinctive signals in detectors. We investigate the ultralight dark matter-induced effects in the gravitational wave interferometers. We perform a systematic computation of the power spectrum of ultralight dark matter in interferometers. We show that the ultralight dark matter-induced effect is most relevant for the interferometers with long baseline and that it is only a sub-leading effect compared to the estimated noise level in the case of Laser Interferometer Space Antenna or future interferometers with an arm-length comparable to a few astronomical units. Gravitational wave interferometers can then place upper limits on the ultralight dark matter density in the solar system. We find that, under certain assumptions, future interferometers with AU-scale arm-length might probe the dark matter density a few hundred times the local dark matter density, which is measured over a much larger spatial scale.

Widespread area estimation of Shinkansen Superexpress Railway noise for noise mapping in Japan

The Environmental Quality Standards for Shinkansen Superexpress Railway (SSR) Noise in Japan stipulate that the maximum noise level measured at a representative point 25 meters away from the railway track is used for evaluation, without any estimation of noise levels, such as road-traffic noise evaluation for the Environmental Quality Standards for Noise in Japan. In this study, a noise estimation model being studied by the Ministry of Environment in Japan was referred and its accuracy was verified by comparing it with actual measurements. A trial estimation of the SSR noise levels was conducted in the area along the SSR line in Osaka Prefecture, Japan. The estimated values were accurate to within a plus or minus 5 dB at 34 out of 56 receiver points when compared to the measured values. However, the accuracy was poor in areas near tunnel entrances and where multiple reflections from noise barriers were present, which were not taken into account in the noise estimation model. The results of the widespread area estimation showed that the noise level variation within an area was significantly affected by the location of buildings in relation to the railway tracks.

Comparative study on interpolation method for road traffic noise level behind buildings in Japanese city blocks for noise mapping

In this study, for reducing the computational load in estimating noise levels behind buildings in Japanese city blocks for noise mapping, 29 interpolation methods were examined for estimating road traffic noise maps for city blocks facing flat roads or elevated roads. As a result, when using the same interpolation method for all city blocks, the RBF -radial basis function- interpolation (radial basis function: thin plate) has the highest interpolation accuracy for city blocks facing flat roads, and the same method has also the highest interpolation accuracy for elevated roads regardless of their height. For safe side estimation, the IDW -inverse distance weighting- method (p=3) is the most appropriate.

Poster] Exploiting high-resolution ADS-B data for flight operation reconstruction towards environmental impact assessment

The estimation of the detrimental impact of civil air traffic on the near-airport environment is conducted by means of dedicated assessment tools, usually based on best-practice methods. However, their application involves availability of information on the flight operations, and, although the use of flight tracking data is very helpful, their full inclusion into these tools is challenging. In this paper, following the authors’ previous efforts and with the purpose of future implementation into assessment tools, highly time-resolved datasets from the OpenSky Network are used for the identification of daily flight operations. Then, the ground track reconstruction is carried out with a newly developed algorithm, which exploits the high time resolution to generate smooth trajectories using only segments and circular arcs while maintaining high accuracy, as shown in the results. This work lays the foundation for immediate future developments involving the reconstruction of aircraft performance and the estimation of airport noise levels.

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field.

Equity of Highway Noise Exposure

This article discusses the inequities in exposure to highway noise, which can have adverse effects on health, productivity, and quality of life. If certain subpopulations tend to live near highways because of structural inequality, gentrification, lack of social capital, or other reasons, then highway noise can contribute to inequity in a systematic way. The authors developed an online mapping application, the Noise Inequity Identification Tool (NIIT), to visualize the noise damage cost of noise pollution from road segments, the noise-equity ratios at the county level, and the population distribution of various demographics. The methodology combines estimates of roadway traffic noise levels with the cost of noise damage from the hedonic pricing literature and estimates of the number of households affected to estimate the noise damage cost for each road segment in the U.S. The cost of the noise damage accruing to each demographic is then aggregated over a larger region and normalized by their proportion of the population to calculate the “noise-equity ratio,” revealing substantial inequities in highway noise exposure between demographics at a variety of spatial scales. By using this tool, planners and practitioners can easily identify regions with existing highway noise exposure inequities and pinpoint roadways with excessive noise damage costs to recommend potential externality mitigation projects.

A Cloud Detection Method for Vertically Pointing Millimeter-Wavelength Cloud Radar.

A new method using three dimensions of cloud continuity, including range dimension, Doppler dimension, and time dimension, is proposed to discriminate cloud from noise and detect more weak cloud signals in vertically pointing millimeter-wave cloud radar observations by fully utilizing the spatiotemporal continuum of clouds. A modified noise level estimation method based on the Hildebrand and Sekhon algorithm is used for more accurate noise level estimation, which is critical for weak signals. The detection method consists of three steps. The first two steps are performed at the Doppler power spectrum stage, while the third step is performed at the base data stage. In the first step, a new adaptive spatial filter combined with the Kuwaraha filter and the Gaussian filter, using the ratio of mean to standard deviation as the adaptive parameter, is applied to initially mask the potential cloud signals to improve the detection performance at the boundary of cloud and noise. Simulations of boundary cases were performed to compare our adaptive filter and normal Gaussian filters. Box filters are used in steps two and three to remove the remaining noise. We applied our method to cloud radar observations with TJ-II cloud radar at the Nanjing University of Information Science & Technology. The results showed that our method can detect more weak cloud signals than the usual methods, which are performed only at the Doppler power spectrum stage or the base data stage.

Noise level estimation based on eigenvalue learning

Noise level estimation based on eigenvalue learning

Estimation of ambient marine noise levels in New South Wales

Marine and offshore infrastructure is increasingly becoming more prominent in NSW resulting in a requirement to investigate the potential behavioral and physiological impacts to marine fauna during construction and operational phases. Typically, detailed modeling is undertaken to compare the predicted underwater noise levels against noise thresholds with limited consideration to the existing ambient noise levels specific to the site. Vessel movements are known to be the primary contributor to ambient underwater noise levels from anthropogenic sources whereas prevailing noise is generally dominated by natural sources such as wind, waves, precipitation, and seismic activity. Underwater noise monitoring of baseline conditions is not always feasible and can also be cost-prohibitive to undertake. Additionally, there is currently limited publicly available underwater noise measurement data along the eastern coastline of NSW, specifically in shallow waters at depths less than 50 m. This study includes simple procedures to allow for preliminary estimates of the key contributors to the marine noise environment in the context of the Eastern coastline of NSW, with the aim of producing maps estimating existing marine noise levels for common descriptors such as SPL(rms) and SEL(24hour).

Fully automated computational measurement of noise in positron emission tomography

ObjectivesTo introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.Methods[18F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI.ResultsIrrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600.ConclusionAn algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.Clinical relevance statementAn automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions.Key Points• Noise is an important quantitative marker that strongly impacts image quality of PET images.• An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.• An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.

Single image noise level estimation by artificial noise

In this paper, we propose a new method for noise level estimation from a single noisy image contaminated by additive white Gaussian noise and gamma noise, which usually appear due to different physical imaging systems. Our method is motivated by the key observation that the curves of small eigenvalues of the patch covariance matrices corresponding to the original noisy image and the artificial noisy image are nearly parallel. Based on some assumptions, we derive a proportional relation equation between eigenvalues and variances of the noisy images in the redundant dimensions. The solution of this equation gives the basic noise estimation formula. However, the assumptions are not strictly satisfied for natural images with textures and fine details. To overcome this drawback, we modified the basic noise estimation formula by introducing a correction parameter. The proposed method is also extended to multiplicative Gamma noise estimation. Numerical results demonstrate that the proposed noise level estimation method outperforms the existing state-of-the-art approaches in accuracy for various scenes. In particular, the decreasing of Root Mean Square Error (RMSE) is an average of over 10% for the testing databases. We further demonstrate that the proposed noise estimation method can be used for blind denoising.

Data quality control tools used to monitor seismoacoustic research arrays in South Korea

Data assessment tools designed to improve data quality and real-time delivery of seismic and infrasound data produced by six seismoacoustic research arrays in South Korea are documented and illustrated. Three distinct types of tools are used including the following: (1) data quality monitoring; (2) real-time station state of health (SOH) monitoring; and (3) data telemetry and archive monitoring. The data quality tools quantify data gaps, seismometer orientation, infrasound polarity, digitizer timing errors, absolute noise levels, and coherence between co-located sensors and instrument-generated signals. Some of the tools take advantage of co-located or closely spaced instruments in the arrays. Digitizer timing errors are identified by continuous estimates of the relative orientation of closely spaced horizontal seismic components based on the root-mean-square error between a reference seismometer and each seismometer in the array. Noise level estimates for seismic and infrasound data are used to assess local environmental effects, seasonal noise variations, and instrumentation changes for maintenance purposes. The SOH monitoring system includes the status of individual ancillary equipment (battery, solar power, or components associated with communication) and provides the operator the capability to compare the current status to the historical data and possibly make remote changes to the system. Finally, monitoring data telemetry and overall data archival provide an assessment of network performance. This collection of tools enables array operators to assess operational issues in near real-time associated with individual instruments or components of the system in order to improve data quality of each seismoacoustic array.

Dear Dr. Schweitzer, Thank you for your email. I believe that you should contact Ms. Borowa-Malinowska (PAN) as mentioned by Prof. Stolarski. We (PWN) are not the holders of the rights in the journal described below. Yours sincerely,Agnieszka Piotrowska-Strojna

The aim of the research was to determine the occurrence of possible, significant levels of infrasound and low frequency noise both in classrooms and around the primary school. Two sources of noise during research were significant: traffic on the national road and a wind farm, located near the school building. So far, few studies have been published regarding the impact of low-frequency, environmental noise from communication routes. The identification of hazards in a form of estimated noise levels resulted in preliminary information whether the location of the school near the road with significant traffic and the nearby wind farm can cause nuisance to children. There have been determined the criteria for assessing infrasound and low frequency noise. There have been made third octave band analyses of noise spectrum and the essential noise indicators were calculated. The results of learning in that school were thoroughly analysed for a long period of time and they were compared to the results obtained in other schools within a radius of 200 km situated near similar noise sources. Chosen assessment criteria show small exposure to low frequency noise. Measured infrasound noise levels are below hearing threshold.

Adaptive image noise level estimation with Chi-square distribution on the flat patches selected by improved PCANet and ResNet101

Additive Gaussian white noise present in images adversely affects their quality, impeding effective human visualization. Current denoising approaches commonly rely on manual setting of the noise level, leading to insufficient noise removal or excessive smoothing. Traditional noise level estimation methods are not adaptive and their generalization ability is limited. To address this issue, this paper developed an adaptive image noise level estimation with Chi-square distribution on the flat patches selected by improved PCANet and ResNet101, including two key procedures: flat image patches selection and noise level estimation. The former was achieved using the improved PCANet and ResNet101 methods while the latter was completed by applying Chi-square distribution to the selected flat patches. Experimental results demonstrated that the reported method achieved high accuracy and powerful generalization ability, making it more suitable for extensive applications compared to existing methods.

Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.

Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning-based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating the local noise level within each region of a CT image. The local noise level will be referred to as a pixel-wise noise map. The SILVER architecture resembled a U-Net convolutional neural network with mean-square-error loss. To generate training data, 100 replicate scans were acquired of 3 anthropomorphic phantoms (chest, head, and pelvis) using a sequential scan mode; 120,000 phantom images were allocated into training, validation, and testing data sets. Pixel-wise noise maps were calculated for the phantom data by taking the per-pixel SD from the 100 replicate scans. For training, the convolutional neural network inputs consisted of phantom CT image patches, and the training targets consisted of the corresponding calculated pixel-wise noise maps. Following training, SILVER noise maps were evaluated using phantom and patient images. For evaluation on patient images, SILVER noise maps were compared with manual noise measurements at the heart, aorta, liver, spleen, and fat. When tested on phantom images, the SILVER noise map prediction closely matched the calculated noise map target (root mean square error <8 Hounsfield units). Within 10 patient examinations, SILVER noise map had an average percent error of 5% relative to manual region-of-interest measurements. The SILVER framework enabled accurate pixel-wise noise level estimation directly from patient images. This method is widely accessible because it operates in the image domain and requires only phantom data for training.

Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

AbstractMany deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice.

The effect of background noise and its removal on the analysis of single-cell expression data

BackgroundIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events.ResultsHere, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3–35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure.ConclusionsOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.

Channel Estimation for Indoor Massive MIMO Visible Light Communication With Deep Residual Convolutional Blind Denoising Network

Massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) has been considered as one of the promising components in the optical wireless communication system. However, the envisioned benefits may be limited due to the high computational complexity to estimate accurate channel state information (CSI). Besides, several propagation paths in the practical communication channels induces significant difficulties. In this paper, a deep residual convolutional blind denoising network (ResCBDNet) has been proposed to predict more realistic channels in indoor m-MIMO VLC communication system. Unlike the other convolutional networks which may overfit on the simplified additive white gaussian noise model, the ResCBDNet exploits noise level estimation subnetwork to improve the generalization ability to real noise as well as interactively reduce the noise in the channel matrix by adjusting the noise level map. More specifically, we have investigated to optimize the ResCBDNet over a large range of SNRs. During the simulation, the sparse channel matrix has been treated as a two dimensional natural image. Simulation results validate that the proposed network is very promising in practical channel estimation for the indoor m-MIMO VLC communication system and outperforms the state-of-the-art channel estimators in terms of normalized mean square error and peak signal-to-noise ratio.

SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.