Topic
Signal-to-noise ratio (imaging)
About: Signal-to-noise ratio (imaging) is a research topic. Over the lifetime, 2119 publications have been published within this topic receiving 42973 citations.
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TL;DR: For the fully connected K user wireless interference channel where the channel coefficients are time-varying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR)=K/2log (SNR)+o(log( SNR), which almost surely has K/2 degrees of freedom.
Abstract: For the fully connected K user wireless interference channel where the channel coefficients are time-varying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR)=K/2log(SNR)+o(log(SNR)) . Thus, the K user time-varying interference channel almost surely has K/2 degrees of freedom. Achievability is based on the idea of interference alignment. Examples are also provided of fully connected K user interference channels with constant (not time-varying) coefficients where the capacity is exactly achieved by interference alignment at all SNR values.
3,385 citations
TL;DR: Both theoretical and computer simulation results show that the SNR improvement factor of this algorithm using multiple band scenes over the single scene of maximum SNR can be substantial and illustrates that the generalized SNR of the test using the full data array is always greater than that of using partial data array.
Abstract: A constant false alarm rate (CFAR) detection algorithm (see J.Y. Chen and I.S. Reed, IEEE Trans. Aerosp. Electron. Syst., vol.AES-23, no.1, Jan. 1987) is generalized to a test which is able to detect the presence of known optical signal pattern which has nonnegligible unknown relative intensities in several signal-plus-noise bands or channels. This test and its statistics are analytically evaluated, and the signal-to-noise ratio (SNR) performance improvement is analyzed. Both theoretical and computer simulation results show that the SNR improvement factor of this algorithm using multiple band scenes over the single scene of maximum SNR can be substantial. The SNR gain of this detection algorithm is compared to the previously published one. It illustrates that the generalized SNR of the test using the full data array is always greater than that of using partial data array. The database used to simulate this adaptive CFAR test is obtained from actual image scenes. >
1,658 citations
TL;DR: A signal-to-noise ratio (SNR) analysis is presented for optical coherence tomography (OCT) signals in which time-domain performance is compared with that of the spectral domain.
Abstract: A signal-to-noise ratio (SNR) analysis is presented for optical coherence tomography (OCT) signals in which time-domain performance is compared with that of the spectral domain. A significant SNR gain of several hundredfold is found for acquisition in the spectral domain. The SNR benefit is demonstrated experimentally in a hybrid time-domain-spectral-domain OCT system.
1,505 citations
TL;DR: The performances of several signal-to noise ratio (SNR) estimation techniques reported in the literature are compared to identify the "best" estimator and some known estimator structures are modified to perform better on the channel of interest.
Abstract: The performances of several signal-to noise ratio (SNR) estimation techniques reported in the literature are compared to identify the "best" estimator. The SNR estimators are investigated by the computer simulation of baseband binary phase-shift keying (PSK) signals in real additive white Gaussian noise (AWGN) and baseband 8-PSK signals in complex AWGN. The mean square error is used as a measure of performance. In addition to comparing the relative performances, the absolute levels of performance are also established; the simulated performances are compared to a published Cramer-Rao bound (CRB) for real AWGN and a CRB for complex AWGN that is derived here. Some known estimator structures are modified to perform better on the channel of interest. Estimator structures for both real and complex channels are examined.
848 citations
TL;DR: The intrinsic and system SNR is applied to predict image SNR and has found satisfactory agreement with measurements on images, which indicates that the initial choice of pixel size is crucial in NMR.
Abstract: The fundamental limit for NMR imaging is set by an intrinsic signal-to-noise ratio (SNR) for a particular combination of rf antenna and imaging subjects. The intrinsic SNR is the signal from a small volume of material in the sample competing with electrical noise from thermally generated, random noise currents in the sample. The intrinsic SNR has been measured for a number of antenna-body section combinations at several different values of the static magnetic field and is proportional to B0. We have applied the intrinsic and system SNR to predict image SNR and have found satisfactory agreement with measurements on images. The relationship between SNR and pixel size is quite different in NMR than it is with imaging modalities using ionizing radiation, and indicates that the initial choice of pixel size is crucial in NMR. The analog of "contrast-detail-dose" plots for ionizing radiation imaging modalities is the "contrast-detail-time" plot in NMR, which should prove useful in choosing a suitable pixel array to visualize a particular anatomical detail for a given NMR receiving antenna.
760 citations