Noise Reduction Filter Research Articles

Statistical Model-Based Noise Reduction Approach for Car Interior Applications to Speech Recognition

This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decisiondirected Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

Mixed constrained image filter design using particle swarm optimization

This article describes an evolutionary image filter design for noise reduction using particle swarm optimization (PSO), where mixed constraints on the circuit complexity, power, and signal delay are optimized. First, the evaluated values of correctness, complexity, power, and signal delay are introduced to the fitness function. Then PSO autonomously synthesizes a filter. To verify the validity of our method, an image filter for noise reduction was synthesized. The performance of the resultant filter by PSO was similar to that of a genetic algorithm (GA), but the running time of PSO is 10% shorter than that of GA.

A Study of the LCMV and MVDR Noise Reduction Filters

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.

Fabrication of an integrated holographic imaging element for a three-dimensional eye-gaze detection system

We evaluate the imaging characteristics of an integrated optical imaging element that is used to obtain images from opposite directions in one imaging sensor for a three-dimensional eye-gaze detection system. The element consists of a transmission-type holographic imaging element, a reflection-type holographic imaging element, and a noise reduction filter. In the evaluation of the imaging characteristics, modulation transfer functions of both the reflection-type and the transmission-type holographic imaging elements are evaluated. Results indicate that both holographic imaging elements have enough resolution, even under white-light illumination conditions, for eye-gaze detection. We also demonstrate the simultaneous detection of images by an artificial eye and objects by using the integrated element under white light or sunlight.

Registration between ultrasound and fluoroscopy or CT in prostate brachytherapy

In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be visualized by fluoroscopy. Intraoperative dosimetry optimization is possible using a combination of TRUS and fluoroscopy, but requires localization of the fluoroscopy-derived seed cloud, relative to the anatomy as seen on TRUS. The authors propose to develop a method of registration of TRUS images and the implants reconstructed from fluoroscopy. A phantom was implanted with 48 seeds then imaged with TRUS and CT. Seeds were reconstructed from CT yielding a cloud of seeds. Fiducial-based ground-truth registration was established between the TRUS and CT. TRUS images are filtered, compounded, and registered to the reconstructed implants by using an intensity-based metric. The authors evaluated a volume-to-volume and point-to-volume registration scheme. In total, seven TRUS filtering techniques and three image similarity metrics were analyzed. The method was also tested on human subject data captured from a brachytherapy procedure. For volume-to-volume registration, noise reduction filter and normalized correlation metrics yielded the best result: An average of 0.54 +/- 0.11 mm seed localization error relative to ground truth. For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: An average of 0.38 +/- 0.19 mm seed localization error relative to the ground truth. In human patient data, C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intraoperative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 +/- 1.26 mm. Fully automated registration between TRUS and the reconstructed seeds performed well in ground-truth phantom experiments and qualitative observation showed adequate performance on early clinical patient data.

A Technique to Improve the Image Quality in Computer Tomography

The purpose of this paper is to assess the effect of different noise reduction filters on computed tomography (CT) images. In particular, denoising filters based on the combination of Gaussian and Prewitt operators and on anisotropic diffusion are proposed. Simulation results show that the proposed techniques increase the image quality and allow the use of a low-dose CT protocol.

A Widely Linear Distortionless Filter for Single-Channel Noise Reduction

Traditionally in the single-channel noise-reduction problem, speech distortion is inevitable since the desired signal is also filtered while filtering the noise. In fact, the more the noise is reduced, the more the speech distortion is added into the desired signal, as proved in the literature. So, if we require no speech distortion, we either end up with no noise reduction at all or have to use multiple sensors. In this paper, we attempt to apply the widely linear (WL) estimation theory to noise reduction. Unlike the traditional approaches that only filter the short-time Fourier transform (STFT) of the noisy signal, the method developed in this paper applies the noise-reduction filter to both the STFT of the noisy signal and its conjugate. With the constraint of no speech distortion, a WL distortionless filter is derived. We show that this new optimal filter can fully take advantage of the noncircularity property of speech signals to achieve up to 3-dB signal-to-noise-ratio (SNR) improvement without introducing any speech distortion, which can only be obtained with the traditional approaches if two or more microphones are used.

Modeling the Effect of Seafloor Ripples on Synthetic Aperture Sonar Speckle Statistics

The characterization and modeling of synthetic aperture sonar (SAS) image statistics is of importance for developing target-on-background detection and classification algorithms and for developing specialized filters for speckle noise reduction. In this paper, we present a model to predict the impact of amplitude scaling caused by seafloor ripples on SAS image speckle statistics. The continuous variation in scattering strength produced by ripples (i.e., ripple-induced changes in seafloor slope) is treated as deterministic amplitude scaling on image speckle produced by the SAS imaging process. Changes in image statistics caused by ripples are quantified in terms of an effective -distribution shape parameter. Agreement between shape parameter estimated from the scaling model and from SAS data collected in experiments off Panama City, FL and off the Ligurian coast near La Spezia, Italy illustrate the efficacy of the model.

Characterization and modeling of synthetic aperture sonar imagery.

The characterization and modeling of synthetic aperture sonar (SAS) image statistics are of importance for developing target‐on‐background detection and classification algorithms and for developing specialized filters for speckle noise reduction. A simple model is presented to predict the impact of amplitude scaling caused by seafloor ripples on SAS image speckle statistics. The continuous variation in scattering strength produced by ripples (i.e., ripple‐induced changes in seafloor slope) is treated as a deterministic amplitude scaling on image speckle produced by the SAS imaging process. Changes in image statistics caused by ripples are quantified in terms of an effective K‐distribution shape parameter. Agreement between shape parameter estimated from the scaling model and from SAS data collected in experiments off of Panama City, FL and off of the Ligurian coast of near La Spezia, Italy illustrates the efficacy of the model. [Work supported by ONR Grant Nos. N00014‐04‐1‐0013 and N00014‐06‐1‐0245.]

On widely linear Wiener and tradeoff filters for noise reduction

Noise reduction is often formulated as a linear filtering problem in the frequency domain. With this formulation, the core issue of noise reduction becomes how to design an optimal frequency-domain filter that can significantly suppress noise without introducing perceptually noticeable speech distortion. While higher-order information can be used, most existing approaches use only second-order statistics to design the noise-reduction filter because they are relatively easier to estimate and are more reliable. When we transform non-stationary speech signals into the frequency domain and work with the short-time discrete Fourier transform coefficients, there are two types of second-order statistics, i.e., the variance and the so-called pseudo-variance due to the noncircularity of the signal. So far, only the variance information has been exploited in designing different noise-reduction filters while the pseudo-variance has been neglected. In this paper, we attempt to shed some light on how to use noncircularity in the context of noise reduction. We will discuss the design of optimal and suboptimal noise reduction filters using both the variance and pseudo-variance and answer the basic question whether noncircularity can be used to improve the noise-reduction performance.

PROGRESS FOR STABLE ARTIFICIAL LIGHTS DISTRIBUTION EXTRVCTION ACCURACY AND ESTIMATION OF ELECTRIC] POWER CONSUMPTION BY MEANS OF DMSP/OLS NIGHTTIME IMAGERY

The Noise Reduction Filter (NRF) that is developed by the authors is applied to extract artificial nightlight components of a time series DMSP/OLS-VIS dataset. High frequency components from the time series DMSP/OLS-VIS dataset are exhausted and a direct current component is extracted by the NRF that is one of the Fourier analysis techniques. The inference of cloud and other disturbance noise are also removed, and a stable artificial nightlight is extracted by the NRF filtration. The intensity value in high power light areas observed by DMSP/OLS-VIS is saturated because of narrow dynamic range of the sensor gain. A simple model called "Deltaic Model" developed by authors corrected those saturated value. Verification of the accuracy of correction methods above described is carried out by comparison with electric power consumption of the calculated values from the model and statistical ones of each prefecture in Japan. Correlation of the values is satisfactory as shown R2 = 0.725. The results of this work shows the remote sensing method by using the DMSP/OLS-VIS nighttime imagery with the correction methods above described is useful to estimate the electric power consumption through a year of fixed areas. Keyword: DMSP/OLS-VIS, NRF filtration, Deltaic Model.

Band-Pass .EPSILON.-Filter for Edge Enhancement and Noise Removal

A band-pass bilateral filter is an improved variant of a bilateral filter that does not have low-pass characteristics but has band-pass characteristics. Unfortunately, its computation time is relatively large since all pixels are subjected to Gaussian calculation. To solve this problem, we pay attention to a nonlinear filter called e-filter and propose an advanced e-filter labeled band-pass e-filter. As e-filter has low-pass characteristics due to spatial filtering, it does not enhance the image contrast. On the other hand, band-pass e-filter does not have low-pass characteristics but has band-pass characteristics to enhance the image contrast around edges unlike e-filter. The filter works not only as a noise reduction filter but also as an edge detection filter depending on the filter setting. Due to its simple design, the calculation cost is relatively small compared to the band-pass bilateral filter. To show the effectiveness of the proposed method, we report the results of some comparison experiments on the filter characteristics and computational cost.

Noise Reduction in CMOS Image Sensor Using Cellular Neural Networks with a Genetic Algorithm

In this paper, Cellular Neural Networks using genetic algorithm (GA-CNNs) are designed for CMOS image noise reduction. Cellular Neural Networks (CNNs) could be an efficient way to apply to the image processing technique, since CNNs have high-speed parallel signal processing characteristics. Adaptive CNNs structure is designed for the reduction of Photon Shot Noise (PSN) changed according to the average number of photons, and the design of templates for adaptive CNNs is based on the genetic algorithm using real numbers. These templates are optimized to suppress PSN in corrupted images. The simulation results show that the adaptive GA-CNNs more efficiently reduce PSN than do the other noise reduction methods and can be used as a high-quality and low-cost noise reduction filter for PSN. The proposed method is designed for real-time implementation. Therefore, it can be used as a noise reduction filter for many commercial applications. The simulation results also show the feasibility to design the CNNs template for a variety of problems based on the statistical image model.

HIT NOISE REDUCTION IN SOME X-RAY IMAGES

The imaging system we have investigating suffer from the appearance of scattered bright points. A usual solution to reduce this noise can be achieved by an increase in exposure but it is very difficult to prevent the hits. In present study, we have investigating algorithms to reduce noise with out an increase in exposure by adopting four digital filters for noise reduction and comparing between efficiency of each filter to determine an efficient method. The resulting images and fidelity criteria showed that the median filter smoothes hit noise while maintaining edge information, but the mean filter image is a blurred version of the noisy image.

Improving the threshold performance of maximum likelihood estimation of direction of arrival

We propose to improve the performance of some direction of arrival (DOA) estimators using array of sensors. We consider those maximum likelihood (ML) estimators that generate some DOA candidates and select one of them through an ML criterion. Our proposal modifies the candidate selection process substituting the traditional sample covariance matrix by a new one computed after filtering the received data with an optimum noise reduction filter. Simulation results indicate an improvement of the performance at low signal-to-noise ratios (SNR) and a considerable reduction of the threshold SNR. The computation of the new selection cost function implies in a small increase in the overall computational effort.

Speech Enhancement Based on Nonlinear Models Using Particle Filters

Motivated by the reportedly strong performance of particle filters (PFs) for noise reduction on essentially linear speech production models, and the mounting evidence that the introduction of nonlinearities can lead to a refined speech model, this paper presents a study of PF solutions to the problem of speech enhancement in the context of nonlinear, neural-type speech models. Several variations of a global model are presented (single/multiple neurons; bias/no bias), and corresponding PF solutions are derived. Different importance functions are given when beneficial, Rao-Blackwellization is proposed when possible, and dual/nondual versions of each algorithms are presented. The method shown can handle both white and colored noise. Using a variety of speech and noise signals and different objective quality measures, the performance of these algorithms are evaluated against other PF solutions running on linear models, as well as some traditional enhancement algorithms. A certain hierarchy in performance is established between each algorithm in the paper. Depending on the experimental conditions, the best-performing algorithms are a classical Rao-Blackwellized particle filter (RBPF) running on a linear model, and a proposed PF employing a nondual, nonlinear model with multiple neurons and no biases. With consistence, the neural-network-based PF outperforms RBPF at low signal-to-noise ratio (SNR).

Low-Dose MDCT Urography: Feasibility Study of Low-Tube-Voltage Technique and Adaptive Noise Reduction Filter

The purpose of this study was to investigate the feasibility of performance of MDCT urography with low tube voltage and an adaptive noise reduction filter. Thirty-one patients underwent excretory phase (300 seconds after administration of 100 mL of iopamidol) 40-MDCT of the urinary tract at 120 and 80 kVp. The 80-kVp images were postprocessed with an adaptive noise reduction filter. Using a 3-point scale for homogeneity of the urinary tract and sharpness of contour, streak artifacts, and overall image quality, two radiologists evaluated coronal multiplanar reconstruction images generated from 120-kVp, unfiltered 80-kVp, and filtered 80-kVp images. Attenuation values of the abdominal aorta, renal pelvis, renal cortex, psoas muscle, vertebral body, and retroperitoneal fat and image noise of the psoas muscle were measured. The effective radiation dose was estimated for each patient. At visual evaluation of images of the upper urinary tract, the quality of filtered 80-kVp images was comparable with that of 120-kVp images. At evaluation of images of the lower urinary tract, however, filtered 80-kVp images were of inferior quality. Except for those of fat tissue, attenuation values were significantly higher on 80-kVp than on 120-kVp images (paired Student's t test, p < 0.01). Noise values did not differ significantly between 120- and filtered 80-kVp images (Dunnett test, p = 0.37). The mean effective doses for 120- and 80-kVp scans were 7.0 and 2.9 mSv. MDCT urography is feasible with a low-tube-voltage technique and an adaptive noise reduction filter. The technique allows reduction in radiation dose without marked degradation of image quality and can be used in clinical assessment of the renal collecting system and upper ureter. For evaluation of the pelvic ureter and urinary bladder, however, image quality is not sufficient, and a compensatory increase in tube current may be necessary.

Improvement on detectability of early ischemic changes for acute stroke using nonenhanced computed tomography: Effect of matrix size

Purpose It has recently been reported that intravenous recombinant tissue plasminogen activator improves the clinical outcome after acute stroke. Computed tomography (CT) is the standard imaging method used to determine the indication for thrombolysis. However, detection of early ischemic change often results in an increase in local radiation exposure. Therefore, the effects of decreased matrix size and use of a noise reduction filter were evaluated. Materials and methods The low contrast resolution was compared for different matrix sizes and imaging filters using a contrast-detail phantom. In addition, early ischemic change in clinical images with matrix sizes of 256 × 256 and 128 × 128 processed using three imaging filters (Gaussian, smoothing, and unsharp mask) from 11 patients within 3 h of stroke onset was evaluated by seven radiologists in a blind manner. Results The use of images with a matrix size of 256 × 256 and processed with the Gaussian filter increased the detection of early signs of acute stroke. Conclusions This study was performed to determine whether the converted matrix size and use of imaging filters could improve the detectability of early ischemic change on CT images in acute stroke. To reduce the dose of radiation exposure for patients, it was effective to use an optimal noise reduction filter and reasonable matrix size. In particular, changing the matrix size to 256 × 256 was the most effective for detection of early ischemic change in examinations using clinical images.

Development of a noise reduction filter algorithm for pediatric body images in multidetector CT.

Recently, several types of post-processing image filter which was designed to reduce noise allowing a corresponding dose reduction in CT images have been proposed and these were reported to be useful for noise reduction of CT images of adult patients. However, these have not been reported on adaptation for pediatric patients. Because they are not very effective with small (<20 cm) display fields of view, they could not be used for pediatric (e.g., premature babies and infants) body CT images. In order to solve this restriction, we have developed a new noise reduction filter algorithm which can be applicable for pediatric body CT images. This algorithm is based on a three-dimensional post processing, in which output pixel values are calculated by multi-directional, one-dimensional median filters on original volumetric datasets. The processed directions were selected except in in-plane (axial plane) direction, and consequently the in-plane spatial resolution was not affected by the filter. Also, in other directions, the spatial resolutions including slice thickness were almost maintained due to a characteristic of non-linear filtering of the median filter. From the results of phantom studies, the proposed algorithm could reduce standard deviation values as a noise index by up to 30% without affecting the spatial resolution of all directions, and therefore, contrast-to-noise ratio was improved by up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of pediatric body CT images.

Improved Reproduction of Stops in Noise Reduction Systems with Adaptive Windows and Nonstationarity Detection

A new block-based noise reduction system is proposed which focuses on the preservation of transient sounds like stops or speech onsets. The power level of consonants has been shown to be important for speech intelligibility. In single-channel noise reduction systems, however, these sounds are frequently severely attenuated. The main reasons for this are an insufficient temporal resolution of transient sounds and a delayed tracking of important control parameters. The key idea of the proposed system is the detection of non-stationary input data. Depending on that decision, a pair of spectral analysis-synthesis windows is selected which either provides high temporal or high spectral resolution. Furthermore, the decision-directed approach for the estimation of the a priori SNR is modified so that speech onsets are tracked more quickly without sacrificing performance in stationary signal regions. The proposed solution shows significant improvements in the preservation of stops with an overall system delay (input-output, excluding group delay of noise reduction filter) of only 10 milliseconds.