Noise Reduction Approach Research Articles

LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density

Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.

A Patch-Based Low-Rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

A Patch-Based Low-Rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

DEVELOPMENT OF A COMPOSITE NOISE MUFFLER FOR SMALL GENERATING SETS

Sound proof generating sets are normally associated with higher power output and it is costly. This is not commonly found in small generating sets as there are often acquired by low income earners and yet they desire the some comfort. This research focuses on the development of a new composite muffler for small generating sets. The design combined the principles of reactive and active noise reduction approach. The newly designed muffler was compared with an existing design and its performance evaluated. Performance tests show an improvement in terms of insertion loss and transmission loss and a reduction in back pressure build-up in the generating set. The average overall efficiency of the existing muffler is 43.28% whereas that of the newly designed composite muffler is 55.83%.The designed composite muffler provides the gasoline generating set with an additional noise reduction (attenuation) of 17.33 dB, reduced back pressure build-up of 3.6 kPa and improvement of 12.09% overall engine efficiency.

Image-Based Angular Distortion Metric of Map Projections by Using Surface Fitting for Noise Reduction

Measuring, analyzing, reducing, and optimizing distortions in map projections is important in cartography. In this study, we introduced a novel image-based angular distortion metric based on the previous spherical great circle arcs-based metric. Images with predefined patterns were used to generate distorted images using mapping software. The generated distorted images with known patterns were then exploited to calculate the proposed angular distortion metric. The mapping software performed the underlying transformation of map projections. Therefore, there was no direct explicit dependence on the forward equations of the map projections in our proposed metric. However, there were fairly large computation errors in the ordinary image-based approach without special correction. To reduce the error, we introduced surface-fitting-based noise reduction in our approach. We established and solved systems of linear equations based on bivariate polynomial functions in the process of noise reduction. Sufficient experiments were made to validate the proposed image-based metric and the accompanying noise reduction approach. In the experiment, the NASA G.Projector was employed as the mapping software for evaluating more than 200 map projections. Experimental results demonstrated that the proposed image-based approach and surface fitting-based noise reduction are feasible and practical for the evaluation of the angular distortion of map projections.

A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model.

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique

ObjectivesTo assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRCP) and breath-hold 3D MRCP (BH-MRCP) with and without denoising procedure using deep learning-based reconstruction (dDLR) at 1.5 T. MethodsForty-two patients underwent MRCP at 1.5 T MRI. The following imaging sequences were performed: Resp-MRCP and BH-MRCP. We applied the dDLR method to the BH-MRCP data (BH-dDLR-MRCP). As a qualitative analysis, two radiologists rated the visibility of the proximal common bile duct (CBD), pancreaticobiliary junction, distal main pancreatic duct, cystic duct, and right and left hepatic ducts. Artifacts and overall image quality were also rated. The signal-to-noise ratios (SNRs), contrast ratios, and contrast-to-noise ratios (CNRs) of the CBD images were calculated for quantitative analysis. ResultsBH-MRCP was successfully performed in a single BH. The qualitative and quantitative measurements for BH-dDLR-MRCP were significantly higher than for BH-MRCP (P < 0.02 and P < 0.001, respectively), and the qualitative measurements for BH-dDLR-MRCP were equivalent to or higher than for Resp-MRCP (P = 0.048–1.000). The SNRs and CNRs for BH-dDLR-MRCP were significantly higher than for Resp-MRCP (P < 0.001 and P = 0.001, respectively). ConclusiondDLR is useful and clinically feasible for BH-MRCP at 1.5 T MRI, and enables rapid imaging without loss of image quality compared to conventional Resp-MRCP.

A noise robust automatic radiolocation animal tracking system

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.

An Adaptive Piecewise Harmonic Analysis Method for Reconstructing Multi-Year Sea Surface Chlorophyll-A Time Series

High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the harmonic analysis of time series (HANTS) method has been widely used to reconstruct geographical variables; however, when applied on multi-year time series over large spatial areas, the optimal harmonic formulas are generally varied in different locations or change across different years. The question of how to choose the optimal harmonic formula is still unanswered due to the deficiency of appropriate criteria. In this study, an adaptive piecewise harmonic analysis method (AP-HA) is proposed to reconstruct multi-year seasonal data series. The method introduces a cross-validation scheme to adaptively determine the optimal harmonic model and employs an iterative piecewise scheme to better track the local traits. Whenapplied to the satellite-derived sea surface chlorophyll-a time series over the Bohai and Yellow Seas of China, the AP-HA obtains reliable reconstruction results and outperforms the conventional HANTS methods, achieving improved accuracy. Due to its generic approach to filling missing observations and tracking detailed traits, the AP-HA method has a wide range of applications for other seasonal geographical variables.

Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points. In this study, the proper orthogonal decomposition (POD) mode calculated from the time-series PSP data is used as a modal basis. Based on the POD modes, the points that effectively represent the features of the pressure distribution are optimally placed by the sensor optimization technique. Then, the time-dependent coefficient vector of the POD modes is determined by minimizing the difference between the time-series pressure data and the reconstructed pressure at the optimal points. Here, the coefficient vector is assumed to be a sparse vector. The advantage of the proposed method is a self-contained method, while existing methods use other data, such as pressure tap data for the reduction of the noise. As a demonstration, we applied the proposed method to the PSP data measuring the Kármán vortex street behind a square cylinder. The reconstructed pressure data agreed very well with the pressures independently measured by pressure transducers. This modal-based approach will be applicable not only to PSP data but other types of experimental data.

Speckle noise reduction and entropy minimization approach for medical images

Speckle noise reduction and entropy minimization approach for medical images

3D Anatomy of the Jaw Muscles of the Spiny‐Tailed Dogfish ( Squalus acanthius )

New technologies are enabling anatomists to see inside animals to better understand the organization and function of the musculoskeletal system. Among the great transformations in vertebrate evolution is the acquisition and diversification of jaws and jaw musculature. Sharks are key species used in anatomy courses as well as research in organismal evolutionary biology where there is considerable diversity in cranial function and feeding behavior in the clade. Here, we use contrast imaging and 3D muscle fiber architecture visualization approaches to model and demonstrate the jaws and jaw muscles of the spinytailed dogfish (Sqaulus acanthius). A specimen was microCTed following immersion in an iodine-ethanol solution. Muscles and cartilages were segmented using Avizo. Muscle fascicles were then modeled using automated methods followed by manual noise reduction approaches. Colors of modeled fascicles were modeled to reflect orientations of muscles to reveal qualitative patterns in fiber organization. These segmented models of fascicles were then projected in quantitative space to show the orientations of muscle fascicles and moments of the quadratomandibularis, preorbitalis and levator hyomandibulae muscles among others. This 3D model will be shared with educators to include in comparative vertebrate anatomy courses. The results from the muscle architecture and mechanics data will be used to compare with other fishes and tetrapods as part of a growing study on the evolution of jaw muscles in vertebrates.

Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach.

Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.

New Insights into Image Restoration Using Filter Analysis and Noise Models

Image restoration, by eliminating noise and blur from an image, restores the original image. In certain cases, image blur is inevitable, and to eliminate blur caused by camera shake or radar imaging or to remove the effect of image system reaction, etc. There are many suggested methods for noise removal and our paper will investigate and address various models of noise and blur and methods of restoration. There are numerous techniques developed, the most efficient being the Wiener filter and is the fundamental noise reduction approach. Wiener filters may cause some undesired effects in image restoration (significant degradation in quality). Various techniques and models are approached in the establishment of the power spectrum of noise and undegraded images. In terms of noise reduction and image restoration, this paper studies the Wiener filter's assumption and quantitative performance improvement. The SNR is improved considerably. But noise reduction is directly proportional to image degradation. To counter this, we must have prior knowledge of the original image by some PDF (Probability Distribution Function).

Effectiveness of Power Electronic Controllers in Mitigating Acoustic Noise and Vibration in High-Rotor Pole SRMs

This paper presents a comparative evaluation of power electronic control approaches for vibro-acoustic noise reduction in High Rotor-Pole Switched Reluctance Machines (HR-SRM). It carries out a fundamental analysis of approaches that can be used to target acoustic noise and vibration reduction. Based on the comprehensive study, four candidates for control have been identified and applied to the HR-SRM drive to evaluate their effectiveness and identify challenges. These four methods include phase advancing, current shaping based on field reconstruction, and random hysteresis band with and without spectrum shaping. The theoretical background, implementation, and vibro-acoustic noise reduction performance of each method are presented in detail. Comparative studies from simulation and experimental measurements have been used to identify the most effective solution to acoustic noise and vibration reduction in HR-SRM configuration.

Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids.

Model-based single image dehazing was widely studied due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model-based single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Gaussian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at the different levels. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art dehazing algorithms.

Experimental investigation on motor noise reduction of Unmanned Aerial Vehicles

Multi-rotor Unmanned Aerial Vehicles (UAVs) are developing rapidly recently in usage and popularity in civil applications. However, although the great benefits UAVs have brought, their noise pollution is one of the concerns with their proliferation. The two main noise sources of a UAV are the motors and propellers. And the propellers are often assumed to be the dominant noise source in the literature, hence noise reduction of propeller becomes research focus; however, recent studies suggest that the contribution from the motor may also be important. This paper explore a passive noise reduction approach based on micro-perforated panel (MPP) absorbers together with sound-proof materials. Experimental results show that the proposed noise reduction constructions can have a significant reduction to the motor's overall noise level.

An optimized non-local LMMSE approach for speckle noise reduction of medical ultrasound images

In this paper, a modified linear-minimum-mean-square-error (LMMSE)-based estimator is presented to reduce speckle noise in ultrasound (US) medical images. In order to significantly improve the performance of the LMMSE estimator, we consider the data redundancy feature which naturally exists in the US images. Since the classical LMMSE method, due to the use of local statistics, cannot perform well in areas where the intensity variation is high, we exploit the similarity between pixels to resolve this problem. In this regards, by using characteristics of the second order local statistics and Pearson distance, an optimum set of similar pixels is selected to be used in the proposed LMMSE-based estimator. Therefore, a good balance between maintaining small details and reducing speckle noise in different regions of US images can be achieved. Quantitative and qualitative results on synthetic and real US data demonstrate that the proposed method yields competitive results in despeckling process compared to the state-of-the-art methods.

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.

To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2-19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed. The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT. Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT. • An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load. • The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT. • This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.

Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control

For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.

A deep-learning-based approach for noise reduction in high-speed optical coherence Doppler tomography.

Optical coherence Doppler tomography (ODT) increasingly attracts attention because of its unprecedented advantages with respect to high contrast, capillary-level resolution and flow speed quantification. However, the trade-off between the signal-to-noise ratio of ODT images and A-scan sampling density significantly slows down the imaging speed, constraining its clinical applications. To accelerate ODT imaging, a deep-learning-based approach is proposed to suppress the overwhelming phase noise from low-sampling density. To handle the issue of limited paired training datasets, a generative adversarial network is performed to implicitly learn the distribution underlying Doppler phase noise and to generate the synthetic data. Then a 3D based convolutional neural network is trained and applied for the image denoising. We demonstrate this approach outperforms traditional denoise methods in noise reduction and image details preservation, enabling high speed ODT imaging with low A-scan sampling density.