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.

A wavelet based statistical approach for speckle reduction in medical ultrasound images

Abstract: The paper introduces a novel speckle reduction method based on soft thresholding the wavelet coefficients of the logarithmically transformed medical ultrasound image. The method is based on the generalized Gaussian distributed (GGD) modeling of subband coefficients. The proposed method is a variant of the recently published BayesShrink method (Chang, G et al., IEEE Trans. Image Processing, vol.9, no.9, p.1522-31, 2000) derived in the Bayesian framework for denoising natural images. It is scale adaptive because the parameters required for estimating the threshold depend on scale and subband data. The threshold is computed by K/spl sigma//sup 2///spl sigma//sub x/ where /spl sigma/ and /spl sigma//sub x/ are the standard deviation of the noise and the subband data of the noise-free image, respectively, and K is a scale parameter. Experimental results show that the proposed method performs better than the median filter as well as the homomorphic Wiener filter, especially in terms of feature preservation for better diagnosis as desired in medical image processing.

Experimental investigations in low-noise trailing-edge design

Abstract: Within a parametric study on brush-type trailing-edge extensions, the noise reduction potential of several design concepts was determined. The obtained database represents the first phase of an ongoing project with the long-term objective to develop scaling laws for a future application of such devices as add-on solutions for today's aircraft components. The experiments comprised both acoustic and aerodynamic measurements on a zero-lift generic plate model (Re = 2.1 x 10 6 to 7.9 × 10 6 ) in DLR's open jet Aeroacoustic Wind Tunnel Braunschweig. Noise data were taken by means of a directional microphone system. Measurement results indicate a significant source noise reduction potential in excess of 10 dB, depending on the configuration. Two relevant noise reduction mechanisms were identified: 1) the suppression of narrowband bluntness noise, as well as 2) the reduction of broadband turbulent boundary-layer trailing-edge noise.

Random noise attenuation by f-x empirical mode decomposition predictive filtering

Abstract: Random noise attenuation always played an important role in seismic data processing. One of the most widely used methods for suppressing random noise was f‐x predictive filtering. When the subsurface structure becomes complex, this method suffered from higher prediction errors owing to the large number of different dip components that need to be predicted. We developed a novel denoising method termed f‐x empirical-mode decomposition (EMD) predictive filtering. This new scheme solved the problem that makes f‐x EMD ineffective with complex seismic data. Also, by making the prediction more precise, the new scheme removed the limitation of conventional f‐x predictive filtering when dealing with multidip seismic profiles. In this new method, we first applied EMD to each frequency slice in the f‐x domain and obtained several intrinsic mode functions (IMFs). Then, an autoregressive model was applied to the sum of the first few IMFs, which contained the high-dip-angle components, to predict the useful ste...

Noise removal using smoothed normals and surface fitting

Abstract: In this work, we use partial differential equation techniques to remove noise from digital images. The removal is done in two steps. We first use a total-variation filter to smooth the normal vectors of the level curves of a noise image. After this, we try to find a surface to fit the smoothed normal vectors. For each of these two stages, the problem is reduced to a nonlinear partial differential equation. Finite difference schemes are used to solve these equations. A broad range of numerical examples are given in the paper.