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measureNoise

Measure noise using Imatest® eSFR chart

Syntax

noiseTable = measureNoise(chart)

Description

example

noiseTable = measureNoise(chart) measures the noise levels using the gray regions of interest (ROIs) of an Imatest® eSFR chart.

Examples

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This example shows how to measure the noise of gray patch ROIs on an eSFR chart.

Read an image of an eSFR chart into the workspace. Linearize the image.

I = imread('eSFRTestImage.jpg');
I_lin = rgb2lin(I);

Create an esfrChart object using the linearized chart image, then display the chart with ROI annotations. The 20 gray patch ROIs are labeled with red numbers.

chart = esfrChart(I_lin);
displayChart(chart);

Measure the noise in all gray patch ROIs.

noiseTable = measureNoise(chart)
noiseTable=20x22 table
    ROI    MeanIntensity_R    MeanIntensity_G    MeanIntensity_B    RMSNoise_R    RMSNoise_G    RMSNoise_B    PercentNoise_R    PercentNoise_G    PercentNoise_B    SignalToNoiseRatio_R    SignalToNoiseRatio_G    SignalToNoiseRatio_B    SNR_R     SNR_G     SNR_B     PSNR_R    PSNR_G    PSNR_B    RMSNoise_Y    RMSNoise_Cb    RMSNoise_Cr
    ___    _______________    _______________    _______________    __________    __________    __________    ______________    ______________    ______________    ____________________    ____________________    ____________________    ______    ______    ______    ______    ______    ______    __________    ___________    ___________

     1     0.85545            0.99547             1.0086            0.35301       0.083743      0.14715        0.13843           0.03284          0.057704          2.4233                  11.887                  6.8544                  7.6881    21.502    16.719    57.175    69.672    64.776    0.08537       0.076881       0.076881   
     2     0.87232            0.98453            0.97984            0.33469        0.14975       0.1523        0.13125          0.058724          0.059725          2.6064                  6.5746                  6.4337                  8.3207    16.357    16.169    57.638    64.624    64.477    0.13565              0              0   
     3      1.0372              1.116             1.1516            0.22956         0.3202      0.36169       0.090022           0.12557           0.14184          4.5183                  3.4852                  3.1839                  13.099    10.845    10.059    60.913    58.022    56.964    0.18162       0.072854       0.072854   
     4      1.9059             2.0097             2.1174             0.3517        0.22062      0.37649        0.13792          0.086516           0.14764          5.4192                  9.1095                   5.624                  14.679     19.19    15.001    57.207    61.258    56.616    0.32882       0.087836       0.087836   
     5      3.1203             4.0434             5.0116             0.5796        0.65321       1.0583         0.2273           0.25616             0.415          5.3836                  6.1901                  4.7357                  14.621    15.834    13.508    52.868     51.83    47.639    0.59331        0.54593        0.50555   
     6      4.2193             5.4594             7.4619             0.7302        0.75399       1.0733        0.28635           0.29568           0.42091          5.7782                  7.2408                  6.9523                  15.236    17.196    16.843    50.862    50.584    47.516    0.69911         0.2886        0.30988   
     7       8.376             10.892             15.463             1.0547          1.274       1.6981        0.41362            0.4996           0.66592          7.9414                  8.5494                  9.1059                  17.998    18.639    19.186    47.668    46.028    43.532     1.0684         0.5369         0.5082   
     8      12.274              15.69             20.872             1.3671         1.4261       1.7696        0.53613           0.55927           0.69394          8.9777                  11.002                  11.795                  19.063    20.829    21.434    45.415    45.048    43.174     1.2247        0.45011        0.55314   
     9      18.901             22.701             30.388             1.5886         1.6768       2.3076        0.62298           0.65757           0.90494          11.898                  13.538                  13.169                  21.509    22.631    22.391     44.11    43.641    40.868     1.4167        0.57663         0.5006   
    10      25.215             31.376             42.889             1.8899         1.9249       2.5649        0.74115           0.75485            1.0058          13.342                    16.3                  16.722                  22.504    24.244    24.466    42.602    42.443     39.95     1.5838        0.62542        0.61931   
    11      37.672             45.462             59.961             2.5106         2.5421       3.2812        0.98453           0.99689            1.2867          15.005                  17.884                  18.274                  23.525    25.049    25.237    40.135    40.027     37.81     2.1486        0.74816        0.59645   
    12      51.662             58.975             73.656             2.9606          3.047       3.7934          1.161            1.1949            1.4876           17.45                  19.355                  19.417                  24.836    25.736    25.764    38.703    38.453     36.55     2.5243        0.93749        0.75463   
    13      70.613             77.105             95.421             3.0924         3.0636       3.9746         1.2127            1.2014            1.5587          22.834                  25.168                  24.007                  27.172    28.017    27.607    38.325    38.406    36.145     2.6906        0.83735        0.61102   
    14      85.824             97.029              113.7             3.9206         3.4895       3.8203         1.5375            1.3684            1.4982           21.89                  27.806                  29.763                  26.805    28.883    29.474    36.264    37.276    36.489     2.9718        0.60018          1.323   
    15      113.94             123.11             136.45             4.0307         3.7118        4.163         1.5807            1.4556            1.6325          28.267                  33.167                  32.776                  29.026    30.414    30.311    36.023    36.739    35.743     3.1562        0.79203        0.86882   
    16      137.31             142.32             153.02             3.5568         3.4969        4.372         1.3948            1.3713            1.7145          38.604                    40.7                      35                  31.733    32.192    30.881     37.11    37.257    35.317     2.8509         1.5228        0.93134   

Display a graph of the mean raw signal and the signal to noise ratio (SNR) of the three color channels over the 20 gray patch ROIs.

figure
subplot(1,2,1) 
plot(noiseTable.ROI, noiseTable.MeanIntensity_R,'r-o', ...
    noiseTable.ROI, noiseTable.MeanIntensity_G,'g-o',noiseTable.ROI, ...
    noiseTable.MeanIntensity_B,'b-o')
title('Signal')
ylabel('Intensity')
xlabel('Gray ROI Number')
grid on
subplot(1,2,2)
plot(noiseTable.ROI, noiseTable.SNR_R,'r-^', noiseTable.ROI, ...
    noiseTable.SNR_G,'g-^',noiseTable.ROI, noiseTable.SNR_B,'b-^')
title('SNR')
ylabel('dB')
xlabel('Gray ROI Number')
grid on

Input Arguments

collapse all

eSFR chart, specified as an esfrChart object.

Output Arguments

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Noise values of each gray patch, returned as a 20-by-22 table. The 20 rows correspond to the 20 gray patches on the eSFR chart. The 22 columns represent the variables shown in the table. Each variable is a scalar of type double.

VariableDescription
ROIIndex of the sampled ROI. The value of ROI is an integer in the range [1, 20]. The indices match the ROI numbers displayed by displayChart.
MeanIntensity_R

Mean value of red channel pixels in the ROI.

MeanIntensity_G

Mean value of green channel pixels in the ROI.

MeanIntensity_B

Mean value of blue channel pixels in the ROI.

RMSNoise_R

Root mean square (RMS) noise of red channel pixels in the ROI.

RMSNoise_G

RMS noise of green channel pixels in the ROI.

RMSNoise_B

RMS noise of blue channel pixels in the ROI.

PercentNoise_RRMS noise of red pixels, expressed as a percentage of the maximum of the original chart image data type.
PercentNoise_GRMS noise of green pixels, expressed as a percentage of the maximum of the original chart image data type.
PercentNoise_BRMS noise of blue pixels, expressed as a percentage of the maximum of the original chart image data type.
SignalToNoiseRatio_RRatio of signal (MeanIntensity_R) to noise (RMSNoise_R) in the red channel.
SignalToNoiseRatio_GRatio of signal (MeanIntensity_G) to noise (RMSNoise_G) in the green channel.
SignalToNoiseRatio_BRatio of signal (MeanIntensity_B) to noise (RMSNoise_B) in the blue channel.
SNR_R

Signal-to-noise ratio (SNR) of the red channel, in dB.

SNR_R = 20*log(MeanIntensity_R/RMSNoise_R).

SNR_G

SNR of the green channel, in dB.

SNR_G = 20*log(MeanIntensity_G/RMSNoise_G).

SNR_B

SNR of the blue channel, in dB.

SNR_B = 20*log(MeanIntensity_B/RMSNoise_B).

PSNR_RPeak SNR of the red channel, in dB.
PSNR_GPeak SNR of the green channel, in dB.
PSNR_BPeak SNR of the blue channel, in dB.
RMSNoise_Y

RMS noise of luminance (Y) channel pixels in the ROI.

RMSNoise_Cb

RMS noise of chrominance (Cb) channel pixels in the ROI.

RMSNoise_Cr

RMS noise of chrominance (Cr) channel pixels in the ROI.

Tips

  • Perform noise measurements on linearized data. Use rgb2lin to linearize sRGB images.

Introduced in R2017b

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