Noise Level Estimation Research Articles

No-reference image noise estimation based on noise level accumulation

In this paper, a method of no-reference image noise assessment is presented, which utilizes the estimated noise level accumulation (NLA) index value. The affine reconstruction model is applied after segmenting the noisy image into several patches. Boundary blur process is conducted to smooth the segmentation edges. For each image patch the mean value standing for brightness and the standard deviation value indicating the noise standard deviation are computed to give the noise samples estimation. The accurate image noise standard deviation is estimated by integrating NLA index value of several overlapped intervals combined with different visual weights. Experiment results are provided to demonstrate that the proposed method performs well for images with different contents over a large range of noise levels both monotonously and accurately. Comparisons against other conventional approaches are also carried out to exhibit the superior performance of the proposed algorithm.

Quality-aware features-based noise level estimator for block matching and three-dimensional filtering algorithm

The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising algorithms (so-called nonblind denoising algorithms), which largely limits the applicability of these nonblind denoising algorithms in many applications. Moreover, these nonblind algorithms do not always achieve the best denoised images in visual quality even when fed with the actual noise level parameter. To address these shortcomings, in this paper we propose a new quality-aware features-based noise level estimator (NLE), which consists of quality-aware features extraction and optimal noise level parameter prediction. First, considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we utilize the marginal statistics of two local contrast operators, i.e., the gradient magnitude and the Laplacian of Gaussian (LOG), to extract quality-aware features. The proposed quality-aware features have very low computational complexity, making them well suited for time-constrained applications. Then we propose a learning-based framework where the noise level parameter is estimated based on the quality-aware features. Based on the proposed NLE, we develop a blind block matching and three-dimensional filtering (BBM3D) denoising algorithm which is capable of effectively removing additive white Gaussian noise, even coupled with impulse noise. The noise level parameter of the BBM3D algorithm is automatically tuned according to the quality-aware features, guaranteeing the best performance. As such, the classical block matching and three-dimensional algorithm can be transformed into a blind one in an unsupervised manner. Experimental results demonstrate that the performance of the BBM3D algorithm is superior to its nonblind version both in visual qualitative evaluations and quantitative comparisons for various images and noise levels.

A Natural-Scene Gradient Distribution Prior and its Application in Light-Microscopy Image Processing

Signal processing in light-microscopy and cell imaging is concerned with reconstructing latent ground truth from imperfect images. This typically requires assuming prior knowledge about the latent ground truth. While this assumption regularizes the problem to an extent where it can be solved, it also biases the result toward the expected. It thus often remains unclear what prior to use for a given practical problem. We argue here that the gradient distribution of natural-scene images may provide a versatile and well-founded prior for light-microscopy images that does not impose assumptions about the geometry of the ground-truth signal, but only about its gradient spectrum. We provide motivation for this choice from different points of view, and we illustrate the resulting regularizer for use on light-microscopy images. We provide a simple parametric model for the resulting prior, leading to efficiently solvable variational problems. We demonstrate the use of these models and solvers in a variety of common image-processing tasks, including contrast enhancement, noise-level estimation, denoising, blind deconvolution, and dehazing. We conclude by discussing the limitations and possible interpretations of the prior.

Boost image denoising via noise level estimation in quaternion wavelet domain

Wavelet thresholding is an important branch of the image denoising field. A key parameter in the algorithms is noise level. As a novel tool of image analysis, quaternion wavelet owns some superior properties compared to discrete wavelets, such as nearly shift-invariant wavelet coefficients and phase based texture presentation. We aim to propose an easy and efficient method to estimate the noise level accurately via quaternion wavelet and further improve the denoising performance. We find that the variance sum of high frequency coefficients of quaternion wavelet is approximately equal to the noise level. However, with the advent of strong edges and/or less smooth regions, this metric would overestimate the noise level. Phases in the quaternion wavelet domain can represent the image texture information. On the premise of detecting smooth regions via phases operation, i.e. without many textures, the proposed noise level estimation method is also suitable to images with complex scenes. The performance of the proposed noise level estimation algorithm is demonstrated superior to classical algorithms. Also, the proposed algorithm can enhance those noise level dependent techniques to improve the denoising performances which are competitive to the state-of-art denoising algorithms.

Image noise level estimation based on higher-order statistics

Noise level estimation is a required step for many preprocessing algorithms in computer vision such as image denoising. In this paper, a model-based technique for additive white Gaussian noise level estimation is proposed via matching moments of eligible transform coefficients of a single image. We assume that noise and image signals are independent and seek to use an image transform that preserves distribution characteristics of noise. This transform should also result in coefficients with a generalized Gaussian distribution for the image itself. We use block-based discrete cosine transform (DCT) and discrete wavelet transform (DWT) which are shown to satisfy these requirements. The proposed method fits the histogram of AC coefficients of all DCT blocks or histogram of all high frequency wavelet coefficients with a generalized Gaussian distribution and attempts to estimate the noise variance through matching the estimated and true values of moments. Since the modeled distributions are symmetric and hence all odd moments are zero, our approach involves solving a nonlinear system of equations based on the method of moments using only even moments. The performance of the proposed method is compared to those of the best prevalent algorithms proposed for noise level estimation using patch-based and model-based techniques. The results on three different image databases show that the proposed scheme outperforms previous techniques in general.

An Automatic Denoising Method with Estimation of Noise Level and Detection of Noise Variability in Continuous Glucose Monitoring

Although continuous glucose monitoring (CGM) devices have been the crucial part of the artificial pancreas, their success has been discounted by random measurement noise. The difficulty of denoising methods for CGM is that the filter parameters are hard to be determined to well reflect the real noise level. Besides, the noise level may show both intraindividual and interindividual variability which thus requires that the filter parameters should be adjusted to follow the noise changes. In this paper, we proposed an automatic denoising method which covers two important components. On the one hand, the noise level can be estimated so that the filter parameters are determined properly. On the other hand, the variability of signal-to-noise ratio can be detected for self-adjustment of filter parameters. First, the noise level is evaluated using expectation maximization algorithm which can fix proper filter parameters for the current signals. Second, a confidence interval is defined by computing the power spectral density (PSD) of the CGM signals to identify the changes of noise level which can tell whether or not the parameters of Kalman filter (KF) should be adjusted. The above issues are investigated based on thirty in silico subjects. The proposed method can work well to identify the changes of noise level and determine proper filter parameters.

Assessing the Underwater Ship Noise Levels in the North Tyrrhenian Sea.

The purpose of this research was to assess the anthropic underwater noise caused by ships within the Cetacean Sanctuary, a wide area in the North Tyrrhenian Sea. Noise from low-frequency continuous sounds has been investigated within the 1/3-octave bands centered at 63 and 125 Hz. All the information about noise sources and sound attenuation have been organized in a database; a tool automatically extracts useful information from it and feeds a ray-tracing model to estimate noise levels. The results show average levels generally over the 100 dB re 1 μPa value.

Geometric validation of MV topograms for patient localization on TomoTherapy

Our goal was to geometrically validate the use of mega-voltage orthogonal scout images (MV topograms) as a fast and low-dose alternative to mega-voltage computed tomography (MVCT) for daily patient localization on the TomoTherapy system. To achieve this, anthropomorphic head and pelvis phantoms were imaged on a 16-slice kilo-voltage computed tomography (kVCT) scanner to synthesize kilo-voltage digitally reconstructed topograms (kV-DRT) in the Tomotherapy detector geometry. MV topograms were generated for couch speeds of 1–4 cm s−1 in 1 cm s−1 increments with static gantry angles in the anterior-posterior and left-lateral directions. Phantoms were rigidly translated in the anterior-posterior (AP), superior-inferior (SI), and lateral (LAT) directions to simulate potential setup errors. Image quality improvement was demonstrated by estimating the noise level in the unenhanced and enhanced MV topograms using a principle component analysis-based noise level estimation algorithm. Average noise levels for the head phantom were reduced by 2.53 HU (AP) and 0.18 HU (LAT). The pelvis phantom exhibited average noise level reduction of 1.98 HU (AP) and 0.48 HU (LAT). Mattes Mutual Information rigid registration was used to register enhanced MV topograms with corresponding kV-DRT. Registration results were compared to the known rigid displacements, which assessed the MV topogram localization’s sensitivity to daily positioning errors. Reduced noise levels in the MV topograms enhanced the registration results so that registration errors were <1 mm. The unenhanced head MV topograms had discrepancies <2.1 mm and the pelvis topograms had discrepancies <2.7 mm. Result were found to be consistent regardless of couch speed. In total, 64.7% of the head phantom MV topograms and 60.0% of the pelvis phantom MV topograms exactly measured the phantom offsets. These consistencies demonstrated the potential for daily patient positioning using MV topogram pairs in the context bony-anatomy based procedures such as total marrow irradiation, total body irradiation, and cranial spinal irradiation.

Applicability of models to estimate traffic noise for urban roads.

BackgroundTraffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic.ResultsFirst, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from −3.5 to 5.5 dB(A).ConclusionsAccording to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.

Rebuilding the credibility of sensor-based camera source identification

The origin information of an image is important in image forensic area. One of the most effective methods to link an image to its source camera is the sensor-based camera source identification (CSI). However, recent studies show that the signature that CSI based on can be easily removed or substituted, which questioned the credibility of the CSI results. To rebuild the credibility of the CSI method, in this paper, we introduce a simple yet effective countermeasure against potential attacks based on noise level estimation. Experimental results show the ability of the proposed method to capture the traces left by anti-forensic methods. Take into account the low complexity, the proposed method is very suitable to be a patch on the traditional CSI method.

Automated measurement of CT noise in patient images with a novel structure coherence feature

While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC = 0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin = 0.75, PCCbetween = 0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine.

A Joint MMSE Channel and Noise Variance Estimation for OFDM/OQAM Modulation

This paper deals with the minimum mean square error (MMSE)-based multipath channel and noise variance estimation in the case of a pilot-aided orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) system. The theoretical expression of the LMMSE channel estimation is formulated and a simpler closed-form is derived. The MSE analysis shows that LMMSE inevitably reaches an error floor due to the interference inherent to OFDM/OQAM modulation. Furthermore, an algorithm for the joint MMSE estimation of both the channel and the noise variance (J-MCNE) is proposed and features a reduced complexity thanks to the low-rank approximation method. The noise level estimation represents a challenge as it is concealed in the intrinsic interferences generated by the OFDM/OQAM. Simulations reveal the capability of the proposed J-MCNE method to estimate the noise variance and show that the achieved bit error rate (BER) is close to that of the perfect estimation.

CogMAC+: A decentralized MAC protocol for opportunistic spectrum access in cognitive wireless networks

The rapidly increasing number of wireless applications are making the wireless spectrum scarcity a serious problem. Cognitive MAC protocols have emerged as a promising solution to address this issue by opportunistically allowing secondary users to utilize unused licensed bands, and enabling spectrum co-existence of multiple secondary networks. In this paper, we present CogMAC+, a decentralized multichannel MAC protocol, which is based on multichannel preamble reservation scheme and CSMA principle. It achieves parallel transmissions for multiple secondary users by enabling them to estimate the duration of channel occupancy of each other. In order to achieve the optimal sensing schedule, it allows secondary users to dynamically access channels according to the channel occupancy of primary users and other secondary networks. Moreover, CogMAC+ uses an adaptive energy detection scheme to dynamically set the energy detection threshold according to the carrier sensing status, the false positive detection ratio and the estimated noise level. CogMAC+ has been verified and evaluated empirically in a software defined radio testbed with realistic spectrum occupancies of licensed users. Our experimental performance evaluation indicates that CogMAC+ is able to carry out reliable parallel communication in multiple channels even in a severely interfered environment.

International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)).

Wavelet-based regularity analysis reveals recurrent spatiotemporal behavior in resting-state fMRI.

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.

Potential of neuro-fuzzy methodology to estimate noise level of wind turbines

Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.

Auto-Adaptive Trigger and Pulse Extraction for Digital Processing in Nuclear Instrumentation

This paper presents a novel auto-adaptive method for pulse triggering and extraction. Pulse triggering uses a threshold that must be placed as close as possible to the noise level. We do this by means of an adaptive threshold level based on real-time noise level estimation. A dynamic estimation of the pulse length is also used for pulse selection. The proposed approach is largely insensitive to noise and enables autonomous extraction of pulses regardless of their shape, height or length. The proposed approach can be used with numerous types of detectors from an analog-to-digital converter, and can be used in conjunction with various pulse processing techniques such as spectroscopy, counting or neutron-gamma discrimination. Moreover, the proposed method enables efficient pulse pile-up detection. This paper demonstrates the feasibility of a completely generic pulse trigger and shows that it compares favorably to a representative panel of other pulse extraction approaches disclosed in the literature.

Blind image noise level estimation using texture-based eigenvalue analysis

Blind noisy image estimation is useful in many visual processing systems. The challenge lies in accurately estimating the image noise level without any priori information of the image. To tackle this challenge, an iterative texture-based eigenvalue analysis approach is proposed in this paper. The proposed approach utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches. Furthermore, a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image. Experimental results are provided to demonstrate that the proposed image noise level estimation approach yields superior accuracy and stability performance to that of conventional noise level estimation approaches, so that to improve the performance of image denoising algorithm.

Online Monaural Speech Enhancement Based on Periodicity Analysis and A Priori SNR Estimation

This paper describes an online algorithm for enhancing monaural noisy speech. First, a novel phase-corrected low-delay gammatone filterbank is derived for signal subband decomposition and resynthesis; the subband signals are then analyzed frame by frame. Second, a novel feature named periodicity degree (PD) is proposed to be used for detecting and estimating the fundamental period ( ${{\rm P}_0}$ ) in each frame and for estimating the signal-to-noise ratio (SNR) in each frame-subband signal unit. The PD is calculated in each unit as the multiplication of the normalized autocorrelation and the comb filter ratio, and shown to be robust in various low-SNR conditions. Third, the noise energy level in each signal unit is estimated recursively based on the estimated SNR for units with high PD and based on the noisy signal energy level for units with low PD. Then the a priori SNR is estimated using a decision-directed approach with the estimated noise level. Finally, a revised Wiener gain is calculated, smoothed, and applied to each unit; the processed units are summed across subbands and frames to form the enhanced signal. The ${{\rm P}_{ 0}}$ detection accuracy of the algorithm was evaluated on two corpora and showed comparable performance on one corpus and better performance on the other corpus when compared to a recently published pitch detection algorithm. The speech enhancement effect of the algorithm was evaluated on one corpus with two objective criteria and showed better performance in one highly non-stationary noise and comparable performance in two other noises when compared to a state-of-the-art statistical-model based algorithm.

Contour let-BasedMethod for Speckle Reduction with Adaptive Estimation of Noise Level

Contour let-BasedMethod for Speckle Reduction with Adaptive Estimation of Noise Level