Noise Density Research Articles

Fuzzy Salt and Pepper Noise Removing for Color Images

Within the field of digital image processing applications, observed images frequently exposed to noise corruption stemming from image acquisition or transmission processes. This noise degradation reduces image quality and yields unfavorable outcomes in subsequent processing stages (e.g., segmentation, pattern recognition, and enhancement). Consequently, the mitigation of noise in images assumes paramount importance in the domain of image processing. This study introduces an algorithm centered around fuzzy logic for removing impulse noise from color images. The efficiency of the proposed algorithms is assessed by comparing their performance against various noise reduction methods. Objective metrics, namely peak signal-to-noise ratio and mean square error, substantiate that the proposed algorithms yield commendable outcomes in noise reduction and the preservation of intricate image details across a wide spectrum of noise densities

Study and implementation of fluxgate current sensor with wireless connectivity for finger exoskeleton rehabilitation

SummaryA fully integrated fluxgate magnetometer has been developed to monitor motor current in a finger exoskeleton device. The use of a fluxgate sensor can provide more accurate and natural movement of the fingers, as well as reducing the weight and complexity of the exoskeleton design. The sensor we developed incorporates several advanced features that provide enhanced sensitivity and accuracy compared to existing state‐of‐the‐art sensors. These features include a fluxgate frontend readout circuit with chopping stabilization technology, which achieves a gain of 14.3 V/T, a low noise density of 4.5 nT/√Hz, and a wide measurement range of ±1.5 mT. The gain variation of the sensor is only 1.5% at an 850 kHz bandwidth, and it has a wide dynamic current range from 0.005 mA to 100 A. The sensor also features an integrated 2.4 GHz wireless circuit for easy data transmission. The use of a fluxgate sensor in monitoring motor torque in the proximal interphalangeal joint can provide more precise and natural finger movement and reduce the weight and complexity of the exoskeleton design.

Quasiparticle Generation-Recombination Noise in the Limit of Low Detector Volume

We have measured the quasiparticle generation-recombination (GR) noise in aluminium lumped element kinetic inductors with a wide range of detector volumes at various temperatures. The basic detector consists of meandering inductor and interdigitated capacitor fingers. The inductor volume is varied from 2 to 153 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu \mathrm{m}^{3}$</tex-math></inline-formula> by changing the inductor width and length to maintain a constant inductance. We started with measuring the power spectrum density (PSD) of the detectors frequency noise which is a function of GR noise and we clearly observed the spectrum roll off at 10 kHz which corresponds to the quasiparticle lifetime. Using data from a temperature sweep of the resonator frequency we convert the frequency fluctuation to quasiparticle fluctuation and observe its strong dependence on detector volume: detectors with smaller volume display less quasiparticle noise amplitude. Meanwhile we observe a saturated quasiparticle density at low temperature from all detectors as the quasiparticle life time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\tau _{qp}}$</tex-math></inline-formula> approaches a constant value at low temperature.

In Vivo Multithread Ultrasound Thermal Strain Imaging.

While thermal therapy is increasingly applied in clinics, real-time temperature monitoring in the target tissue can facilitate improvements in the planning, controlling, and evaluating of therapeutic procedures. Thermal strain imaging (TSI), based on tracking the echo shifts in ultrasound images, has great potential for temperature estimation as is demonstrated in vitro. However, due to physiological motion-induced artifacts and estimation errors, employing TSI for in vivo thermometry is still challenging. Building on our earlier development of respiration-separated TSI (RS-TSI), a multithread TSI (MT-TSI) approach is proposed as the first part of a bigger plan. A flag image frame is first identified by analyzing the correlation between ultrasound images. Then, the quasi-periodic phase profile of respiration is determined and split into multiple parallelly distributed periodical subranges. Multiple threads of independent TSI calculations are thus established, with image matching, motion compensation, and thermal strain estimation performed in each thread. Finally, after applying temporal extrapolation, spatial alignment, and interthread noise suppression, the TSI results obtained in different threads are averaged to obtain the merged output. In microwave (MW) heating experiments targeting porcine perirenal fat, the thermometry accuracy of MT-TSI is comparable to that of RS-TSI, while the former exhibits lower noise and higher temporal density.

An Adaptive Fuzzy C-Means Noise Image Segmentation Algorithm Combining Local and Regional Information

Noisy image segmentation using fuzzy c-mean clustering (FCM) algorithm is an important direction in the field of image segmentation, among which FCM using local information or non-local information constraints has made some progress. However, as the noise density increases, the local information cannot restore the real image. The non-local information-based segmentation method overly retains the noise and thus produces false edges resulting in the degradation of image segmentation accuracy. To address these problems, this paper use the regional and local information to develop an adaptive FCM image segmentation algorithm(RLFCM). First, the regional information and local information are obtained by the regional information filtering method and the local information filtering method. Then the weighted image is constructed in a certain way. Secondly, to make the objective function adaptively adjust the constraint ratio of the original image and the weighted image pixel by pixel, the degree of constraint is adaptively changed by using the difference between them as the coefficients of both through certain transformation. Finally, to speed up the convergence of the objective function, we add the number of clustering samples to the denominator of the objective function by certain transformations to reduce the number of iterations of the objective function. The proposed algorithm achieves more than 90% in image segmentation accuracy (SA), and mean Intersection-over-Union (mIoU) through synthetic and real-world image tests and acquires good performance in other indexes.

Lead‐free pyroelectric infrared detector based on (Bi1/2Na1/2)TiO3‐BaTiO3 ferroelectric ceramics

AbstractIn this paper, we theoretically and experimentally reported a lead‐free pyroelectric infrared (PIR) detector using (Bi1/2Na1/2TiO3)‐BaTiO3(BNT‐BT) ferroelectric ceramics as the sensitive material. The variation of noise density, voltage response rate (RV), and specific detection rate (D*) with the modulation frequency under the current mode amplification circuit was investigated, and it was found that the lead‐free PIR detector showed high RV in the low frequency band. The RV and D* reached 1.51 × 105 V/W and 2.02 × 108 cmHz1/2W−1 at 10 Hz, respectively. The results were much superior to the PIR based on traditional commercial pyroelectric ceramics, indicating that BNT‐BT lead‐free ceramics have great potential in application to PIR detectors.

A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.

Noise-robustness test for ultrasound breast nodule neural network models as medical devices.

Deep learning technology has been widely applied to medical image analysis. But due to the limitations of its own imaging principle, ultrasound image has the disadvantages of low resolution and high Speckle Noise density, which not only hinder the diagnosis of patients' conditions but also affect the extraction of ultrasound image features by computer technology. In this study, we investigate the robustness of deep convolutional neural network (CNN) for classification, segmentation, and target detection of breast ultrasound image through random Salt & Pepper Noise and Gaussian Noise. We trained and validated 9 CNN architectures in 8617 breast ultrasound images, but tested the models with noisy test set. Then, we trained and validated 9 CNN architectures with different levels of noise in these breast ultrasound images, and tested the models with noisy test set. Diseases of each breast ultrasound image in our dataset were annotated and voted by three sonographers based on their malignancy suspiciousness. we use evaluation indexes to evaluate the robustness of the neural network algorithm respectively. There is a moderate to high impact (The accuracy of the model decreased by about 5%-40%) on model accuracy when Salt and Pepper Noise, Speckle Noise, or Gaussian Noise is introduced to the images respectively. Consequently, DenseNet, UNet++ and Yolov5 were selected as the most robust model based on the selected index. When any two of these three kinds of noise are introduced into the image at the same time, the accuracy of the model will be greatly affected. Our experimental results reveal new insights: The variation trend of accuracy with the noise level in Each network used for classification tasks and object detection tasks has some unique characteristics. This finding provides us with a method to reveal the black-box architecture of computer-aided diagnosis (CAD) systems. On the other hand, the purpose of this study is to explore the impact of adding noise directly to the image on the performance of neural networks, which is different from the existing articles on robustness in the field of medical image processing. Consequently, it provides a new way to evaluate the robustness of CAD systems in the future.

Estimation of instability induced by probe laser frequency noise in cold-atom microwave clocks

The influence of laser frequency noise on the stability of a compact cold-atom microwave clock was investigated theoretically in this study. The phase-noisy laser was characterized by a phase-diffusion field. Given the laser linewidth, the analytical expressions of the atomic covariance function and the noise power spectral density of the relative intensity noise transferred from the frequency noise were derived based on stochastic differential equations. Our results provide an effective method to estimate the impact of laser frequency noise on the stability of an integrating sphere cold atomic clock. This method can be extended to other cold atom detection cases.

Field-programmable-gate-array-based digital frequency stabilization of low-phase-noise diode lasers.

We present the comparison of a field-programmable-gate-array (FPGA) based digital servo module with an analog counterpart for the purpose of laser frequency stabilization to a high-finesse optical cavity. The transfer functions of both the digital and analog modules for proportional-integral-derivative control are measured. For the lasers stabilized to the cavity, we measure the singe-sideband power spectral density of fast phase noise by means of an optical beat with filtered light transmitted through the cavity. The comparison between the digital and analog modules is performed for two low-phase-noise diode lasers at 1120 and 665 nm wavelengths. The performance of the digital servo module compares well to the analog one for the lowest attained levels of 30 mrad for the integrated phase noise and 10-3 for the relative noise power. The laser linewidth is determined to be in the sub-kHz regime, only limited by the high-finesse cavity. Our work exploits the versatility of the FPGA-based servo module (STEMlab) when used with open-source software and hardware modifications. We demonstrated that such modules are suitable candidates for remote-controlled low-phase-noise applications in the fields of laser spectroscopy and atomic, molecular, and optical physics.

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the cosmic microwave background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization, a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.

Low-Frequency Noise of 4H-SiC CMOS Technology for Analog ICs

To design low-noise analog ICs for nuclear power plants, low-frequency noise (LFN) of 4H-SiC CMOS technology was studied in the frequency range of 3 Hz–10 kHz at room temperature. The LFN of the long-channel MOS devices was systematically studied in terms of gate length, gate width, drain current, and drain voltage. It was found to follow the noise model for carrier number fluctuation regardless of conduction type. In addition, the standard deviation of spectral noise density ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}_{\text {id}}$ </tex-math></inline-formula> ) at 10 Hz for the p-channel MOS was experimentally found to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.6\times $ </tex-math></inline-formula> larger than that for the n-channel MOS. This was caused by the increase in random telegraph noise (RTN) caused by oxide hole traps with nitrogen incorporation at the SiO2/SiC interface. The flicker noise coefficient (KF) in the noise model was estimated to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${4.1} \times {10}^{-{26}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.2} \times {10}^{-{27}}\,\,\text{s}^{\text {1-EF}}\text{A}^{\text {2-AF}}\text{F}^{^{^{^{}}}}$ </tex-math></inline-formula> for the n- and p-channel MOS, respectively.

Supervised Attention Network for Arbitrary-Shaped Text Detection in Edge-Fainted Noisy Scene Images

Text mining in noisy situations, like poor contrast and fainted edges, is one of the challenging areas of research in the domain of social networks and computer vision. Scene text detection is a complicated task as text regionhaving varying span in term of size, orientation, aspect ratio, color, font, and script. Furthermore, the contrast of a scene image varies drastically in noisy situations due to poor illumination and image filtering. This faints the text edges and make the task of detection more challenging. In this article, we bring forward a semantic edge supervised spatial-channel attention network, known as SESANet, for detecting arbitrary-shaped text instances in noisy scene images with faint text edges. Our network learns multiscale (MS) supervised edge semantic, pixel-wise spatial structure information, and interchannel dependencies for precisely localizing the text masks in scene images with poor contrast and illumination. Our network is efficient, precise, and fast in nature. SESANet captures rich, dense, discriminative, and MS semantic information. The experimental results show the success of the proposed network. It shows a superior performance with regard to recall on the publicly available benchmark datasets. A new dataset scene images, named as Edge-fainted Noisy Arbitrary-shaped Scene Text (EFNAST) dataset, having varying noise density, poor contrast, low illumination, and faint edges is created.

Robustness improvement of a nitrogen-vacancy magnetometer by a double driving method.

The nitrogen vacancy (NV) color center in diamonds is an electron spin that can measure magnetic fields with high sensitivity and resolution. Furthermore, the robustness of an NV-based quantum system should be improved for further application in other sensing methods and in the exploration of basic physics. In this work, the robustness of an NV magnetometer is improved by the double driving method. The sensitivity of the NV magnetometer was improved 2.1 times by strengthening the pumping power from 100 to 600 mW. In this process, thermal drift was introduced, which affects the measurement accuracy. The temperature drift of a diamond matrix was measured using an infrared camera, and the temperature change of a diamond host drifted to ∼80K under high laser and microwave power. To address the drift of temperature owing to sensitivity improvement by pumping enhancement, the double driving method was introduced, to suppress the drift of the resonance frequency, to improve the robustness of a continuous-wave NV magnetometer. The magnetic noise density was improved from 10 to 1.2 nT/Hz1/2. This study checked the source of temperature noise in the process of measuring with the NV color centers and proposes a double driving measurement method to track the resonant frequency change due to environmental temperature drift and improve sensitivity. The findings of this study are useful in applying complex pulse protocols in high-level sensing applications based on solid-state spin.

VOX2BIM+ - A Fast and Robust Approach for Automated Indoor Point Cloud Segmentation and Building Model Generation

Building Information Modeling (BIM) plays a key role in digital design and construction and promises also great potential for facility management. In practice, however, for existing buildings there are often either no digital models or existing planning data is not up-to-date enough for use as as-is models in operation. While reality-capturing methods like laser scanning have become more affordable and fast in recent years, the digital reconstruction of existing buildings from 3D point cloud data is still characterized by much manual work, thus giving partially or fully automated reconstruction methods a key role. This article presents a combination of methods that subdivide point clouds into separate building storeys and rooms, while additionally generating a BIM representation of the building’s wall geometries for use in CAFM applications. The implemented storeys-wise segmentation relies on planar cuts, with candidate planes estimated from a voxelized point cloud representation before refining them using the underlying point data. Similarly, the presented room segmentation uses morphological operators on the voxelized point cloud to extract room boundaries. Unlike the aforementioned spatial segmentation methods, the presented parametric reconstruction step estimates volumetric walls. Reconstructed objects and spatial relations are modelled BIM-ready as IFC in one final step. The presented methods use voxel grids to provide relatively high speed and refine their results by using the original point cloud data for increased accuracy. Robustness has proven to be rather high, with occlusions, noise and point density variations being well-tolerated, meaning that each method can be applied to data acquired with a variety of capturing methods. All approaches work on unordered point clouds, with no additional data being required. In combination, these methods comprise a complete workflow with each singular component suitable for use in numerous scenarios.

Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

Image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise’s influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing; The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image’s main edge points, and remove the detail edge points and noise points; nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. Image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging.

Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.

The impact of temperature on thermal fluctuations in magnetic nanoparticle systems

We investigate the effect of temperature on the thermal magnetic noise signal of magnetic nanoparticle (MNP) systems as models for non-interacting macrospins. An analytical expression for the amplitude of the fluctuations in a magnetic field is derived for the Brownian and the Néel fluctuation mechanisms and compared with numerical results at different temperatures. To experimentally validate our findings, magnetic noise spectra of two commercially available polydisperse MNP systems (Ferucarbotran and Perimag) were measured at different, biomedically relevant temperatures. A distinctive effect of temperature on the power spectral noise densities is measurable already for 5 K temperature differences and, within the bandwidth of the experiment, higher noise amplitudes are found for lower temperatures. However, a crossing of the spectra at higher frequencies is revealed in simulations so that the total fluctuation amplitude is conserved. These findings contribute to a profound understanding of temperature influences on MNP fluctuation and relaxation mechanisms.

Ultra-low noise bipolar current source for ultracold atom magnetic system.

We report the development of an ultralow-noise bipolar current source based on the configuration of H-bridge current switching. The measured relative current noise fluctuation reaches 4 × 10-9Hz-1/2, which enables an ultra-stable magnetic system for cold atom experiments. We avoid the influence of the AC leakage currents induced by the large parasitic capacitance of the H-bridge. First, the current sensor is placed as close as possible to the magnetic coils so that the systematic errors from these leakage currents are minimized. Second, the large parasitic capacitance, which parallels the magnetic coils and forms an LC oscillator, is removed from the feedback loop in our setup to maintain a large self-resonance frequency of the feedback control loop. These two improvements lead to a current source that is more precise and less noisy. Remarkably, the lowest current noise density produced by the proposed method is only 500 nA Hz-1/2 at a current of 100A, which is about ten fold smaller than the case with leakage current. To optimize the feedback control, a numerical simulation is implemented by using Matlab Simulink, and the numerical simulation results are entirely consistent with the experimental results.

The Q[formula omitted] high performance LH[formula omitted] target

A high-power liquid hydrogen target was built for the Jefferson Lab Qweak experiment, which measured the tiny parity-violating asymmetry in e→p scattering at an incident energy of 1.16 GeV, and a Q2=0.025 GeV2. To achieve the luminosity of 1.7×1039 cm−2 s−1, a 34.5 cm-long target was used with a beam current of 180 μA. The ionization energy-loss deposited by the beam in the target was 2.1 kW. The target temperature was controlled to within ±0.02 K and the target noise (density fluctuations) near the experiment’s beam helicity-reversal rate of 960 Hz was only 53 ppm. The 58 liquid liter target achieved a differential pressure (head) across the pump of 7.6 kPa (11.4 m) and a mass flow of 1.2 ± 0.3 kg/s (corresponding to a volume flow of 17.4 ± 3.8 l/s) at the nominal 29 Hz rotation frequency of the recirculating centrifugal pump. We describe aspects of the design, operation, and performance of this target, the highest power LH2target ever used in an electron scattering experiment to date.