- Signal to noise ratio (image processing)
The

**Signal to Noise Ratio**(**SNR**) is used in image processing as a physical measure of thesensitivity of animaging system . Industry standards measure**SNR**indecibels (dB) and therefore apply the 20 log rule to the "pure"**SNR**ratio. In turn, yielding the "sensitivity." Industry standards measure and define sensitivity in terms of theISO film speed equivalent; SNR:32.04 dB = excellent image quality and SNR:20 dB = acceptable image quality. [*ISO 12232: 1997 Photography – Electronic Still Picture Cameras – Determining ISO Speed here*]**Definition of SNR**Due to

**"clamping"**in modern imaging sensors and systems the classic definition of**SNR**has become less meaningful. Traditionally, the definition of**SNR**has been defined as the ratio of the average signal value to thestandard deviation of the signal value.$SNR\; =\; frac\{mu,Sig\}\{sigma,\_\{mu,Sig$

However, because of clamping, $\{sigma,\_\{mu,Sig$ approaches zero and thus,**SNR**approaches infinity; which is physically meaningless in image analysis [*Electro Optical Industries, Inc.(2005). EO TestLab Methodology. In "Education/Ref". http://www.electro-optical.com/html/toplevel/educationref.asp.*] . Therefore, a new definition of**SNR**yields a meaningful value.**SNR**is thus defined as the ratio of the net signal value to the**RMS noise**. Where the net signal value is the difference between the average signal and background values, and the**RMS noise**is the standard deviation of the signal value.$SNR\; =\; frac\{Signal\}\{RMS\; Noise\},$

**Calculations****Explanation**The line data is gathered from the arbitrarily defined signal and background regions and inputed into an

array (refer to image to the right). To calculate the average signal and background values, a second order polynomial is fitted to the array of line data and subtracted from the original array line data. This is done to remove anytrends . Finding the mean of this data yields the average signal and background values. The net signal is calculated from the difference of the average signal and background values. The**RMS**orroot mean square noise is defined from the signal region. Finally,**SNR**is determined as the ratio of the net signal to the**RMS noise.****Polynomial and coefficients***The second order polynomial is calculated by the follwing double sumation.

$f\_i\; =\; sum\_\{j=0\}^m\; sum\_\{i=1\}^n\; a\_j\; x\_i^j$

**$f,$ = output sequence

**$m,$ = the polynomial order

**$x,$ = the input sequence (array/line values) from the signal region or background region, respectively.

**$n,$ = the number of lines

**$a\_j,$ = the polynomial fitcoefficients

*The polynomial fit coefficients can thus be calculated by asystem of equations . [*Aboufadel, E.F., Goldberg, J.L., Potter, M.C. (2005)."Advanced Engineering Mathematics (3rd ed.)."New York, New York: Oxford University Press*]

$egin\{bmatrix\}\; 1\; x\_1\; x\_1^2\; \backslash \; 1\; x\_2\; x\_2^2\; \backslash \; vdots\; vdots\; vdots\; \backslash \; 1\; x\_n\; x\_n^2end\{bmatrix\}$egin{bmatrix} a_2 \ a_1 \ a_0 \end{bmatrix}=egin{bmatrix} f_1 \ f_2 \ vdots \ f_n end{bmatrix}

*Which can be written...

$egin\{bmatrix\}\; n\; sum\; x\_i\; sum\; x\_i^2\; \backslash \; sum\; x\_i\; sum\; x\_i^2\; sum\; x\_i^3\; \backslash \; sum\; x\_i^2\; sum\; x\_i^3\; sum\; x\_i^4\; end\{bmatrix\}$egin{bmatrix} a_2 \ a_1 \ a_0 end{bmatrix}=egin{bmatrix} sum f_i \ sum f_i x_i \ sum f_i x_i^2end{bmatrix}

*Computer software or rigourousrow operations will solve for the coefficients.**Net signal, signal, and background***The second order polynomial is subtracted from the original data to remove any trends and then averaged. This yields the signal and background values.

$mu,Sig\; =\; frac\{sum\_\{i=1\}^n\; (X\_i\; -\; f\_i)\}\{n\}\; qquad\; qquad\; mu,Bckrnd\; =\; frac\{sum\_\{i=1\}^n\; (X\_i-f\_i)\}\{n\}$

**$mu,Sig$ = average signal value

**$mu,Bckrnd$ = average background value

**$n,$ = number of lines in background or signal region

**$X\_i,$ = value of the i^{th}line in the signal region or backround region, respectively.

**$f\_i,$ = value of the i^{th}output of the second order polynomial.

*Hence, the net signal value is determined.

$Signal,\; =\; mu,Sig\; -\; mu,Bckrnd$**RMS noise and SNR***The

**RMS Noise**is defined as thesquare root of theabsolute value of the sum of variances from the signal region. [*Electro Optical Industries, Inc.(2005). EO TestLab Methodology. In "Education/Ref". http://www.electro-optical.com/html/toplevel/educationref.asp.*]

$RMS\; Noise\; =\; sqrt\{Bigg|frac\{sum\_\{i=1\}^n\; (X\_i-frac\{sum\_\{i=1\}^n\; X\_i\}\{n\})^2\}\{n\}Bigg$

*The SNR is thus given by the definition.$SNR\; =\; frac\{Signal\}\{RMS\; Noise\},$

*Using the industry standard 20 log rule [*Test and Measurement World (2008). SNR. In "Glossary and Abbreviations".http://www.tmworld.com/info/CA6436814.html?q=SNR*] ...

$SNR\; =\; 20\; log\_\{10\}\; frac\{Signal\}\{RMS\; Noise\},$**See also*** Minimum resolvable contrast

*Minimum resolvable temperature difference

*Modulation transfer function

*Signal to noise ratio

*Signal transfer function **References****External links*** http://www.electro-optical.com/html/

* http://www.iso.org/iso/home.htm

* http://www.tmworld.com/

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