Rician noise

Magnetic resonance imaging is a medical imaging technique that measures the response of atomic nuclei of body tissues to high frequency radio waves when placed in a strong magnetic field and that produces images of the internal organs. Denoising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution.. In this paper, an improved de-noising technique is proposed on Magnetic Resonance Images highly corrupted with Rician Noise using wave atom shrinkage.

Magnetic resonance imaging is a medical imaging technique that measures the response of atomic nuclei of body tissues to high frequency radio waves when placed in a strong magnetic field and that produces images of the internal organs. Denoising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution.. In this paper, an improved de-noising technique is proposed on Magnetic Resonance Images highly corrupted with Rician Noise using wave atom shrinkage.

This paper addresses maximum likelihood estimation of images corrupted by a Rician noise, with the aim to propose an efficient optimization method. The application example is the restoration of magnetic resonance images. Starting from the fact that the criterion to minimize is non-convex but unimodal, the main contribution of this work is to propose an optimization scheme based on the majorization-minimization framework after introducing a variable change allowing to get a strictly convex criterion. The resulting descent algorithm is compared to the classical MM descent algorithm and its performances are assessed using synthetic and real MR images. Finally, by combining these two MM algorithms, two optimization strategies are proposed to improve the numerical efficiency of the image restoration for any signal-to-noise ratio.

This paper addresses maximum likelihood estimation of images corrupted by a Rician noise, with the aim to propose an efficient optimization method. The application example is the restoration of magnetic resonance images. Starting from the fact that the criterion to minimize is non-convex but unimodal, the main contribution of this work is to propose an optimization scheme based on the majorization-minimization framework after introducing a variable change allowing to get a strictly convex criterion. The resulting descent algorithm is compared to the classical MM descent algorithm and its performances are assessed using synthetic and real MR images. Finally, by combining these two MM algorithms, two optimization strategies are proposed to improve the numerical efficiency of the image restoration for any signal-to-noise ratio.

— We propose a novel variational method for Magnetic Resonance Image denoising which is based on the modelling of Rician noise. In the context of MAP estimation the resulting energy functional is minimized embedding its Euler-Lagrange equation in an iterative regularization scheme. The algorithm is tested with synthetic MR images and the results show the effectiveness of the method which provides high contrast denoised images.

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En este trabajo se revisan fundamentos teóricos del denoising mediante manipulación de coeficientes wavelet para imágenes de resonancia magnética cerebrales. Se seleccionan diferentes técnicas presentes en la bibliografía citada para el umbralizado de los coeficientes y se evalúan los resultados obtenidos a partir de la implementación en Matlab.

The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multi-exponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced. To deal with the large-scale optimization problem, a Majoration-Minimization approach coupled with an adapted non-linear least squares algorithm is implemented. The proposed algorithm is numerically fast, stable and leads to accurate results. Its effectiveness is illustrated by an application to a simulated phantom and to magnitude multi spin echo MRI images acquired from a tomato sample.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier data transmission, where a single stream of information is divided over a large number of subcarriers. The primary purpose of this work was to find out the relationships connecting BER performance in noisy environments and the number of transmitted subcarriers. In order to simulate this kind of environment, various noise types where taken into consideration such as complex Rayleigh fading, complex rician noise, AWGN and phase noise.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier data transmission, where a single stream of information is divided over a large number of subcarriers. The primary purpose of this work was to find out the relationships connecting BER performance in noisy environments and the number of transmitted subcarriers. In order to simulate this kind of environment, various noise types where taken into consideration such as complex Rayleigh fading, complex rician noise, AWGN and phase noise.

Wireless systems contain transmitters and receivers in order to exchange information. In the past decades, this exchange of information was conducted with the use of only one modulated carrier (frequency). As wireless technology evolved, new techniques emerged. One of them was unique for its capability of significant lower data loss compared to others. The main concept of the new technique, commonly known as Orthogonal Frequency Division Multiplexing, was the utilization of various orthogonal carriers (frequencies) which were multiplexed with each other in order to prevent system breakdown in the case of frequency fading. In this paper we simulated our enhanced OFDM platform based on Turbo Coding (TC) in the presence of a noisy channel (AWGN, Phase noise, Rician noise, ITU PA3) exhibiting excellent BER performance.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier data transmission, where a single stream of information is divided over a large number of subcarriers. The primary purpose of this work was to find out the relationships connecting BER performance in noisy environments and the number of transmitted subcarriers. In order to simulate this kind of environment, various noise types where taken into consideration such as complex Rayleigh fading, complex rician noise, AWGN and phase noise.

Wireless systems contain transmitters and receivers in order to exchange information. In the past decades, this exchange of information was conducted with the use of only one modulated carrier (frequency). As wireless technology evolved, new techniques emerged. One of them was unique for its capability of significant lower data loss compared to others. The main concept of the new technique, commonly known as Orthogonal Frequency Division Multiplexing, was the utilization of various orthogonal carriers (frequencies), which were multiplexed with each other in order to prevent system breakdown in the case of frequency fading. In this paper we simulated our enhanced OFDM platform based on Turbo Coding (TC) in the presence of a noisy channel (AWGN, Phase noise, Rician noise, ITU PA3) exhibiting excellent BER performance.

In Orthogonal Frequency Division Multiplexing (OFDM) carriers are orthogonally related and hence no guard band is necessary like in Frequency Division Multiplexing (FDM). So spectrum's of users can overlap which enhances the spectrum efficiency of the network. In this thesis one of the main concern of OFDM, inter carrier interference (ICI) is considered using different window functions in frequency domain in pulse shaping of OFDM data symbols which are considered uncorrelated. It reduces the inter carrier interference (ICI) power into simply the square magnitude of window function. Here we have taken different window functions for comparing performance of the wireless links in content of ICI power and desired received signal. Orthogonal Frequency Division Multiplexing (OFDM) is technique of multi-carrier modulation in which a single high rate data-stream is first divided into multiple low rate data-streams and is then modulated using sub-carriers. These sub-carriers are orthogonal to each other. Its main advantages are multipath delay spread tolerance, high spectral efficiency, immunity to Frequency Selective Fading Channels, efficient modulation and demodulation process which is performed by computationally efficient Inverse Fast Fourier Transform (IFFT) and Fast Fourier Transform (DFT) operation respectively. An important disadvantage of OFDM is the carrier frequency offset which disturbs the orthogonality among the carriers and results Inter carrier interference (ICI). The undesired ICI degrades the performance of the system.

Keywords: — Frequency Spectrum, SIR, Power of Desired Signal, Fading and AWGN, OFDM, ICI.