Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In the proposed method median filter is modified by adding more features, and the quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal- to-no noise ratio (SNR) and root mean square error (RMSE).
Abstract: In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms. Key words : Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE. © 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. doi:10.3329/jsr.v3i1.5544 J. Sci. Res. 3 (1), 81-89 (2011)
88 citations
TL;DR: This work model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field and a second independent hGMRF and shows that parameter estimation for this model is feasible and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images.
Abstract: The local statistical properties of photographic images, when represented in a multi-scale basis, have been described using Gaussian scale mixtures. Here, we use this local description as a substrate for constructing a global field of Gaussian scale mixtures (FoGSM). Specifically, we model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field (hGMRF) and a second independent hGMRF. We show that parameter estimation for this model is feasible, and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images. We develop an algorithm for removing additive Gaussian white noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.
88 citations
TL;DR: A recursive filter for IR is introduced, which conserves the statistical properties of the measured data while pre-processing attenuation measurements, and is shown to successfully eliminate streaking artifacts in photon-starved situations.
Abstract: Computed Tomography (CT) screening and pediatric imaging, among other applications, demand the development of more efficient reconstruction techniques to diminish radiation dose to the patient. While many methods are proposed to limit or modulate patient exposure to x-ray at scan time, the resulting data is excessively noisy, and generates image artifacts unless properly corrected. Statistical iterative reconstruction (IR) techniques have recently been introduced for reconstruction of low-dose CT data, and rely on the accurate modeling of the distribution of noise in the acquired data. After conversion from detector counts to attenuation measurements, however, noisy data usually deviate from simple Gaussian or Poisson representation, which limits the ability of IR to generate artifact-free images. This paper introduces a recursive filter for IR, which conserves the statistical properties of the measured data while pre-processing attenuation measurements. A basic framework for inclusion of detector electronic noise into the statistical model for IR is also presented. The results are shown to successfully eliminate streaking artifacts in photon-starved situations.
87 citations
TL;DR: The adaptive noise-reduction system that includes the UNANR model can effectively eliminate random noise in ambulatory ECG recordings, leading to a higher SNR improvement than that with the same system using the popular least-mean-square (LMS) filter.
87 citations
03 Jun 2007
TL;DR: In this paper, the authors demonstrate two analog photonic links that use different noise reduction techniques to achieve high gain and low noise figure without electronic amplification, both links use a high-power, low-noise master oscillator power amplifier as the optical source, a balanced-bridge dual-output LiNbO3 Mach-Zehnder modulator with a record low Vpi = 1.33 V at 12 GHz.
Abstract: We demonstrate two analog photonic links that use different noise reduction techniques to achieve high gain and low noise figure without electronic amplification. Both links use a high-power, low-noise master oscillator power amplifier as the optical source, a balanced-bridge dual-output LiNbO3 Mach-Zehnder modulator with a record low Vpi = 1.33 V at 12 GHz, and either one or two high-power rear-illuminated photodetectors. In the first link, both outputs of the quadrature-biased modulator are used to illuminate two photodetectors configured for laser noise cancellation, yielding record high gain (> 17.0 dB) and low noise figure ( 12.7 dB) but also record low noise figure (< 5.7 dB) across this same frequency band.
87 citations