Signal-to-Noise Ratio Estimation in Passive Correlation-Based Imaging

Signal-to-Noise Ratio Estimation in Passive Correlation-Based Imaging

We consider here the problem of imaging using passive incoherent recordings due to ambient noise sources. The first step towards imaging in this configuration is the computation of the cross-correlations of the recorded signals. These cross-correlations are computed between pairs of sensors (receivers) and they contain very important information about the background medium. Indeed, it was shown...

Signal-to-Noise Ratio Estimation

Of the many measures that characterize the performance of a communication receiver, signal-to-noise ratio (SNR) is perhaps the most fundamental in that many of the other measures directly depend on its knowledge for their evaluation. In the design of receivers for autonomous operation, it is desirable that the estimation of SNR take place with as little known information as possible regarding o...

Single-image signal-to-noise ratio estimation.

A method for estimating the signal-to-noise ratio from a single image is presented in this paper. The autocorrelation-based technique requires that image details be correlated over distances of a few pixels, while the noise is assumed to be uncorrelated from pixel to pixel. The latter is shown to be a good approximation in the case of scanning electron microscope (SEM) images provided that the ...

Prediction of signal‐to‐noise ratio gain for passive higher‐order correlation detection of energy transients

Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging.

A general theory of signal-to-noise ratio (SNR) in simultaneous acquisition of spatial harmonics (SMASH) imaging is presented, and the predictions of the theory are verified in imaging experiments and in numerical simulations. In a SMASH image, multiple lines of k-space are generated simultaneously through combinations of magnetic resonance signals in a radiofrequency coil array. Here, effects ...