# sigmaSNR: Determine noise scale levels from specified *S*ignal to... In mwaved: Multichannel Wavelet Deconvolution with Additive Long Memory Noise

## Description

Compute the noise scale levels for each channel using the Signal to Noise Ratios

## Usage

 1 sigmaSNR(signal, SNR) 

## Arguments

 signal Noisefree multichannel input signal SNR A numeric vector specifying the desired Signal to Noise Ratio for each channel.

## Details

The output noise scale levels (theoretical standard deviation for the process noise process in each channel) is governed by the blurred Signal-to-Noise Ratio (SNR) measured in decibels (dB) where,

SNR = 10 log_{10} (\frac{||k*f||^2}{σ^2)}

and k*f is the blurred signal, ||\cdot|| is the norm operator and σ is the standard deviation of the noise. Roughly speaking, noise levels are considered high, medium and low for the cases 10 dB, 20 dB and 30 dB respectively.

## Value

A numeric vector with m elements giving the scales (standard deviation of the noise in each channel) to achieve the desired SNR.

multiNoise multiSigma
 1 2 3 4 5 6 7 8 9 n <- 1024 m <- 3 signal <- makeLIDAR(n) blur <- gammaBlur(n, c(0.5, 0.75, 1), rep(1, m)) X <- blurSignal(signal, blur) SNR <- 10*1:3 sigma <- sigmaSNR(X, SNR) E <- multiNoise(n, sigma) sigmaEst <- multiSigma(E)