Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Wavelet-Domain Medical Image Denoising Using Bivariate Laplacian Mixture Model

Abstract: In this paper, we proposed novel noise reduction algorithms that can be used to enhance image quality in various medical imaging modalities such as magnetic resonance and multidetector computed tomography. The noisy captured 3-D data are first transformed by discrete complex wavelet transform. Using a nonlinear function, we model the data as the sum of the clean data plus additive Gaussian or Rayleigh noise. We use a mixture of bivariate Laplacian probability density functions for the clean data in the transformed domain. The MAP and minimum mean-squared error (MMSE) estimators allow us to efficiently reduce the noise. The employed prior distribution is mixture and bivariate, and thus accurately characterizes the heavy-tail distribution of clean images and exploits the interscale properties of wavelets coefficients. In addition, we estimate the parameters of the model using local information; as a result, the proposed denoising algorithms are spatially adaptive, i.e., the intrascale dependency of wavelets is also well exploited in the enhancement process. The proposed approach results in significant noise reduction while the introduced distortions are not noticeable as a result of accurate statistical modeling. The obtained shrinkage functions have closed form, are simple in implementation, and efficiently enhances data. Our experiments on CT images show that among our derived shrinkage functions usually BiLapGausMAP produces images with higher peak SNR. However, BiLapGausMMSE is preferred especially for CT images, which have high SNRs. Furthermore, BiLapRayMAP yields better noise reduction performance for low SNR MR datasets such as high-resolution whole heart imaging while BiLapGauMAP results in better performance in MR data with higher intrinsic SNR such as functional cine data.

Modified LMS algorithms for speech processing with an adaptive noise canceller

Abstract: A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. A major disadvantage of the LMS algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power, This leads to degrading performance when the desired signal exhibits large power fluctuations and is a serious problem in many speech processing applications. This work considers two modified LMS algorithms, the weighted sum and sum methods, designed to solve this problem by reducing the size of the steps in the weight update equation when the desired signal is strong. The weighted sum method is derived from an optimal method (also developed in this work), which is not generally applicable because it requires quantities unavailable in a practical system. The previously proposed, but ad hoc, sum method is analyzed and compared to the weighted sum method. Analysis of the two modified LMS algorithms indicates that either one provides substantial improvements in the presence of strong desired signals and similar performance in the presence of weak desired signals, relative to the unmodified LMS algorithm. Computer simulations with both uncorrelated Gaussian noise and speech signals confirm the results of the analysis and demonstrate the effectiveness of the modified algorithms. The modified LMS algorithms are particularly suited for signals (such as speech) that exhibit large fluctuations in short-time power levels.

The Airframe Noise Reduction Challenge

Abstract: The NASA goal of reducing external aircraft noise by 10 dB in the near-term presents the acoustics community with an enormous challenge. This report identifies technologies with the greatest potential to reduce airframe noise. Acoustic and aerodynamic effects will be discussed, along with the likelihood of industry accepting and implementing the different technologies. We investigate the lower bound, defined as noise generated by an aircraft modified with a virtual retrofit capable of eliminating all noise associated with the high lift system and landing gear. However, the airframe noise of an aircraft in this 'clean' configuration would only be about 8 dB quieter on approach than current civil transports. To achieve the NASA goal of 10 dB noise reduction will require that additional noise sources be addressed. Research shows that energy in the turbulent boundary layer of a wing is scattered as it crosses trailing edge. Noise generated by scattering is the dominant noise mechanism on an aircraft flying in the clean configuration. Eliminating scattering would require changes to much of the aircraft, and practical reduction devices have yet to receive serious attention. Evidence suggests that to meet NASA goals in civil aviation noise reduction, we need to employ emerging technologies and improve landing procedures; modified landing patterns and zoning restrictions could help alleviate aircraft noise in communities close to airports.

Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT.

Abstract: Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since image noise cannot be fully characterized on the sole basis of the noise level at computed tomography (CT). Noise spatial correlation (or noise texture) is closely related to the detection and characterization of low-contrast objects and may be quantified by analyzing the noise power spectrum. High-contrast spatial resolution can be measured using the modulation transfer function and section sensitivity profile and is generally unaffected by noise reduction. Detectability of low-contrast lesions can be evaluated subjectively at varying dose levels using phantoms containing low-contrast objects. Clinical applications with inherent high-contrast abnormalities (eg, CT for renal calculi, CT enterography) permit larger dose reductions with denoising techniques. In low-contrast tasks such as detection of metastases in solid organs, dose reduction is substantially more limited by loss of lesion conspicuity due to loss of low-contrast spatial resolution and coarsening of noise texture. Existing noise reduction strategies for dose reduction have a substantial impact on lowering the radiation dose at CT. To preserve the diagnostic benefit of CT examination, thoughtful utilization of these strategies must be based on the inherent lesion-to-background contrast and the anatomy of interest. The authors provide an overview of existing noise reduction strategies for low-dose abdominopelvic CT, including analytic reconstruction, image and projection space denoising, and iterative reconstruction; review qualitative and quantitative tools for evaluating these strategies; and discuss the strengths and limitations of individual noise reduction methods.

A fully integrated noise robust strategy for the identification of constitutive laws from digital images

Abstract: Constitutive parameter identification has been greatly improved by the achievement of full-field measurements. In this context, noise sensitivity has been shown to be of great importance. It is crucial to incorporate noise sensitivity minimization in the design of robust identification procedures. In this paper, we investigate noise sensitivity reduction techniques for constitutive parameter identification based on Finite Element Model Updating. After examining the existing strategies, we propose a single step algorithm based on a mixed optical/mechanical cost function. The key point of this novel procedure is that no boundary conditions are needed. A first example on a real case illustrates the advantages of the proposed methodology in terms of noise sensitivity. A second example shows its capabilities to identify a non-linear consitutive law. Copyright © 2010 John Wiley & Sons, Ltd.