Extremely low count detection for EELS spectrum imaging by reducing CCD read-out noise

Background: Many studies on the efficacy of directional microphones (DIRMs) and noise reduction (NR)algorithms were not conducted under realistic signal-to-noise ratio (SNR) conditions. A Repeat–RecallTest (RRT) was developed previously to partially address this issue.<br />Purpose: This study evaluated whether the RRT could provide a more comprehensive understanding ofthe efficacy of a DIRM and NR algorithm under realistic SNRs. Possible interaction with listener workingmemory capacity (WMC) was assessed.<br />Research Design: This study uses a double-blind, within-subject repeated measures design.<br />Study Sample: Nineteen listeners with a moderate degree of hearing loss participated.<br />Data Collection and Analysis: The RRT was administered with participants wearing the study hearingaids (HAs) under two microphones (omnidirectional versus directional) by two NR (on versus off) conditions.Speech was presented from 0° at 75 dB SPL and a continuous noise from 180° at SNRs of 0, 5,10, and 15 dB. The order of SNR and HA conditions was counterbalanced across listeners. Each testcondition was completed twice in two 2-hour sessions separated by one month.<br />Results: The recall scores of listeners were used to group listeners into good and poor WMC groups.Analysis using linear mixed-effects models revealed significant effects of context, SNR, and microphonefor all four measures (repeat, recall, listening effort, and tolerable time). NR was only significant on thelistening effort scale in the DIRM mode at an SNR of 5 dB. Listeners with good WMC performed better onall measures of the RRT and benefitted more from context. Although DIRM benefitted listeners with goodand poor WMC, the benefits differed by context and SNR.<br />Conclusions: The RRT confirmed the efficacy of DIRM and NR on several outcome measures underrealistic SNRs. It also highlighted interactions between WMC and sentence context on feature efficacy.<br />

Impact of SNR, masker type and noise reduction processing on sentence recognition performance and listening effort as indicated by the pupil dilation response

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

Receiver Operating Characteristic (ROC) curves can be used to describe detectability of noise-limited signals. The basic concepts underlying the theory of ROC curves and the experimental determination of ROC curves are discussed. ROC curves are used to describe the detectability of the image of 2 mm Lucite beads (similar to certain kinds of small gall-stones) in a noisy background of radiographic mottle. Results are shown for four typical radiographic screen-film combinations of differing optical and noise properties. The qualitative relationship between the ROC curves and the mathematical model describing radiographic mottle is examined. The ROC curves describing the detectability of the bad images when radiographed with two screen-film combinations of equal system speed but different optical characteristics are compared to the quality of blood vessel images obtained with the same screen-film combinations. It is concluded that it is important to distinguish between the assessment of image quality based on the relative sharpness of high contrast images, versus the assessment of image quality based on the relative detectability of low contrast noise-limited images.

Many studies on the efficacy of directional microphones (DIRMs) and noise-reduction (NR) algorithms were not conducted under realistic signal-to-noise ratio (SNR) conditions. A Repeat-Recall Test (RRT) was developed previously to partially address this issue. This study evaluated whether the RRT could provide a more comprehensive understanding of the efficacy of a DIRM and NR algorithm under realistic SNRs. Possible interaction with listener working memory capacity (WMC) was assessed. This study uses a double-blind, within-subject repeated measures design. Nineteen listeners with a moderate degree of hearing loss participated. The RRT was administered with participants wearing the study hearing aids (HAs) under two microphones (omnidirectional versus directional) by two NR (on versus off) conditions. Speech was presented from 0° at 75 dB SPL and a continuous noise from 180° at SNRs of 0, 5, 10, and 15 dB. The order of SNR and HA conditions was counterbalanced across listeners. Each test condition was completed twice in two 2-hour sessions separated by one month. The recall scores of listeners were used to group listeners into good and poor WMC groups. Analysis using linear mixed-effects models revealed significant effects of context, SNR, and microphone for all four measures (repeat, recall, listening effort, and tolerable time). NR was only significant on the listening effort scale in the DIRM mode at an SNR of 5 dB. Listeners with good WMC performed better on all measures of the RRT and benefitted more from context. Although DIRM benefitted listeners with good and poor WMC, the benefits differed by context and SNR. The RRT confirmed the efficacy of DIRM and NR on several outcome measures under realistic SNRs. It also highlighted interactions between WMC and sentence context on feature efficacy.

In real-world environments, the signals captured by a set of microphones in a speech communication system are mixtures of the desired signal, interference, and ambient noise. A promising solution for proper speech acquisition (with reduced noise and interference) in this context consists in using the linearly constrained minimum variance (LCMV) beamformer to reject the interference, reduce the overall mixture energy, and preserve the target signal. The minimum variance distortionless response beamformer (MVDR) is also commonly known to reduce the interference-plus-noise energy without distorting the desired signal. In either case, it is of paramount importance to accurately quantify the achieved noise and interference reduction. Indeed, it is quite reasonable to ask, for instance, about the price that has to be paid in order to achieve total removal of the interference without distorting the target signal when using the LCMV. Besides, it is fundamental to understand the effect of the MVDR on both noise and interference. In this correspondence, we investigate the performance of the MVDR and LCMV beamformers when the interference and ambient noise coexist with the target source. We demonstrate a new relationship between both filters in which the MVDR is decomposed into the LCMV and a matched filter (MVDR solution in the absence of interference). Both components are properly weighted to achieve maximum interference-plus-noise reduction. We investigate the performance of the MVDR, LCMV, and matched filters and elaborate new closed-form expressions for their output signal-to-interference ratio (SIR) and output signal-to-noise ratio (SNR). We theoretically demonstrate the tradeoff that has to be made between noise reduction and interference rejection. In fact, the total removal of the interference may severely amplify the residual ambient noise. Conversely, totally focussing on noise reduction leads to increased level of residual interference. The proposed study is finally supported by several numerical examples.

Stochastic resonance (SR) method can be applied to enhance the signal-to-noise ratio (SNR) of a system output using the unavoidable environmental noise. However, the robustness of SR and the corresponding parameters selection are not agreeable. This paper develops a two-step approach to detect faults in bearings. The first step is done by applying the Duffing oscillator to reveal the weak periodic signals and hence the judgment of existence of faults in mechanical systems. SR is then employed in the second step using Duffing equation as the bistable system. Two barrier parameters in the Duffing equation based bistable system in the second step are obtained using an artificial bee colony (ABC) algorithm. The comparison study between the present two-step method and traditional SR is performed by a numerical simulation signal of cosine function with colored noise. The fault detection results show that the presented method is favored to detect and diagnose rolling bearing fault from a strong noise environment.

Loss of understanding speech in noise, known as signal to noise ratio (SNR) loss, is the largest problem of most hearing aid users today. This paper discusses the most important noise reduction (NR) agents available in current hearing aids and their ability to improve SNR-loss. Then, it is investigated how the many different NR agents can best be integrated to maximize results. A global minimization shows that a (Generalized Sidelobe Canceller) beamformer followed by single microphone NR agents is the theoretically optimal configuration. Since the performance of the different NR agents depends on the environment, it is suggested to use an environmental classifier to select the optimal combination of NR agents. Either by a fixed set of rules or by using a self training network catered to the specific SNR-loss of the hearing impaired.

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Neutron stimulated emission computed tomography (NSECT) uses photons emitted from inelastic scattering of neutrons with biological objects to quantify the elemental composition of the object and reconstruct an image. Previously, NSECT has been used to detect liver and breast disease in vivo. In this study, we investigated the capability of imaging brain tumors using NSECT. A GEANT4 simulation was developed to model the brain, skull, and a spherical lesion. Images corresponding to phosphorus, sulfur, and iron (both individually and as combinations) were generated from a simulated NSECT scan. Signal-to-noise ratio (SNR) and full width at half maximum (FWHM) in the tumor region were calculated to assess image accuracy (FWHM ≤ 5% error) and detectability (SNR > 2.5). The scan with the least amount of absorbed dose required to achieve these criteria was defined as the optimal acquisition. The lowest dose value was found to be 0.0837 cGy for a 2 cm brain tumor imaged using a single germanium detector, 6 equally spaced angles from 0 to 180 degrees, 20 projections per angle and 0.5 million neutrons per projection. The SNR for the combination of phosphorus, sulfur, and iron with the given condition was 9.288 and FWHM for the iron was 15 mm with the given condition. In conclusion, NSECT is capable of imaging a 2 cm brain tumor using the elemental composition of phosphorus, sulfur, and iron with reasonable SNR, FWHM and radiation dose.

PurposeQ.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT.MethodsThe NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region.ResultsBased on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast.ConclusionThe BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion.

For greater advancement in future communication, efficient noise reduction algorithms with lesser complexity are a necessity. Noise in audio signal poses a great challenge in speech recognition, speech communication, speech enhancement and transmission. Hence the most efficient algorithm for noise reduction must be chosen in such a way that the cost for noise removal is a less as possible, but a large portion of noise is removed. The common method for the removal of noise is optimal linear filtering method, and some algorithms in this method are Wiener filtering, Kalman filtering and spectral subtraction technique. Here, the noise signal is passed through a filter or transformation. However, due to the complexity of these algorithms, there are better algorithms like Signal Dependent Rank Order Mean algorithm (SD-ROM), which removes noise from audio signals and retains the characteristics of the signal. The algorithm can be adjusted depending on the characteristics of noise signal too. To remove white Gaussian noise, discrete wavelet transform technique is used. After each of the techniques are applied to the samples, SNR and elapsed time are calculated. All of the above techniques show an increased Signal to Noise Ratio (SNR) after processing, as seen in the simulation results.

Electro-optical sensors can be of significant aid to our law enforcement agencies particularly if their capabilities and limitations are fully understood. In the following, the imaging process is discussed as it applies to the needs and requirements of security, surveillance and law enforcement. Our approach is to associate quantitative signal-to-noise ratios with simple geometric images as developed by electro-optical sensors, to determine the observer's SNR needs through psychophysical experimentation and then, through further psychophysical experimentation, to correlate the detectability of these simple images with the visual discrimination of the images of real objects. The visual discrimination tasks we consider are simple image detection and the higher order tasks of recognition and identification. The concepts developed form a rational basis for the selection of electro-optical equipments which have a reasonable expectation of actually performing a desired function.

A theoretical and experimental comparison of photon-countingcameras and CCD's for use in astronomical speckle imaging wasperformed. Photon-counting cameras able to detect single-photonevents typically exhibit a lower quantum efficiency (QE) and suffersaturation effects at high light levels. In contrast, CCD's offera high QE and virtually unlimited photon-count rate. However CCD'sare limited at lower light levels by noise associated with the readoutprocess. Speckle-imaging performance was quantified by derivationof the signal-to-noise ratio (SNR) of the power spectrum and theKnox-Thompson product to include CCD readout noise. Ground-basedtelescope observations at various light levels were obtained with anadvanced, high-speed, low-noise CCD camera to verify SNRexpressions. The useful operating ranges for these two camera typeswere compared by consideration of the effects of QE, readout noise, andmaximum photon-count rate. Although photon-counting camerascontinued to dominate low-light-level applications, CCD's are shown tooffer significant improvements over photon-counting cameras for a widerange of light levels. Future reductions of readout noise will further improve CCD speckle-imaging performance.