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    <title>MATLAB Central Newsreader - How to discover image noise in MATLAB?</title>
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    <item>
      <pubDate>Fri, 07 Mar 2008 15:16:33 -0500</pubDate>
      <title>How to discover image noise in MATLAB?</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/165269#419627</link>
      <author>emre baykal</author>
      <description>Hello there. i am a senior student in electrical &lt;br&gt;
engineering. My term Project is about to identify source &lt;br&gt;
camera. While identifing, i should use image noise for &lt;br&gt;
comparing all images that i took before. What i am asking &lt;br&gt;
is how i can find or obtain the noise of any image. i have &lt;br&gt;
looked at all topics and workouts about this subject. All &lt;br&gt;
of them are about denoising or removing the noise. it is &lt;br&gt;
useless for me at this time. Anyway, if you have any idea &lt;br&gt;
or if you did any Project like this before can you tell me &lt;br&gt;
how i can obtain the noise of an image in MATLAB. Thanks.&lt;br&gt;
</description>
    </item>
    <item>
      <pubDate>Fri, 07 Mar 2008 21:44:45 -0500</pubDate>
      <title>Re: How to discover image noise in MATLAB?</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/165269#419704</link>
      <author>Ashish Uthama</author>
      <description>&amp;gt; While identifing, i should use image noise for&lt;br&gt;
&amp;gt; comparing all images that i took before.&lt;br&gt;
&lt;br&gt;
Am sorry, I do not follow.&lt;br&gt;
&lt;br&gt;
&lt;br&gt;
You would want a so-called 'gold' standard to compare against to get the  &lt;br&gt;
noise. Maybe you could get an 'ideal' image and then compare the test  &lt;br&gt;
image against it? (you can then use various measure: MAD etc)&lt;br&gt;
&lt;br&gt;
googling for&lt;br&gt;
&amp;nbsp;&amp;nbsp;&amp;nbsp;image noise estimation&lt;br&gt;
might help&lt;br&gt;
&lt;br&gt;
&lt;br&gt;
On Fri, 07 Mar 2008 10:16:33 -0500, emre baykal  &lt;br&gt;
&amp;lt;20393289@mail.baskent.edu.tr&amp;gt; wrote:&lt;br&gt;
&lt;br&gt;
&amp;gt; Hello there. i am a senior student in electrical&lt;br&gt;
&amp;gt; engineering. My term Project is about to identify source&lt;br&gt;
&amp;gt; camera. While identifing, i should use image noise for&lt;br&gt;
&amp;gt; comparing all images that i took before. What i am asking&lt;br&gt;
&amp;gt; is how i can find or obtain the noise of any image. i have&lt;br&gt;
&amp;gt; looked at all topics and workouts about this subject. All&lt;br&gt;
&amp;gt; of them are about denoising or removing the noise. it is&lt;br&gt;
&amp;gt; useless for me at this time. Anyway, if you have any idea&lt;br&gt;
&amp;gt; or if you did any Project like this before can you tell me&lt;br&gt;
&amp;gt; how i can obtain the noise of an image in MATLAB. Thanks.&lt;br&gt;
&lt;br&gt;
</description>
    </item>
    <item>
      <pubDate>Sat, 08 Mar 2008 01:15:01 -0500</pubDate>
      <title>Re: How to discover image noise in MATLAB?</title>
      <link>http://www.mathworks.com/matlabcentral/newsreader/view_thread/165269#419730</link>
      <author>ImageAnalyst</author>
      <description>On Mar 7, 10:16=A0am, "emre baykal" &amp;lt;20393...@mail.baskent.edu.tr&amp;gt;&lt;br&gt;
wrote:&lt;br&gt;
&amp;gt; Hello there. i am a senior student in electrical&lt;br&gt;
&amp;gt; engineering. My term Project is about to identify source&lt;br&gt;
&amp;gt; camera. While identifing, i should use image noise for&lt;br&gt;
&amp;gt; comparing all images that i took before. What i am asking&lt;br&gt;
&amp;gt; is how i can find or obtain the noise of any image. i have&lt;br&gt;
&amp;gt; looked at all topics and workouts about this subject. All&lt;br&gt;
&amp;gt; of them are about denoising or removing the noise. it is&lt;br&gt;
&amp;gt; useless for me at this time. Anyway, if you have any idea&lt;br&gt;
&amp;gt; or if you did any Project like this before can you tell me&lt;br&gt;
&amp;gt; how i can obtain the noise of an image in MATLAB. Thanks.&lt;br&gt;
&lt;br&gt;
-------------------------&lt;br&gt;
emre baykal:&lt;br&gt;
There are two kinds of noise: spatial and temporal.  The simplest way&lt;br&gt;
to look at temporal noise if to take several snapshots of a constant&lt;br&gt;
scene, then to take the average of the standard deviations at each&lt;br&gt;
pixel.  For example, take 100 frames.  For each pixel, calculate the&lt;br&gt;
standard deviation of the 100 signal values.  Then average all those&lt;br&gt;
standard deviations together.  There are several sources of temporal&lt;br&gt;
noise: shot noise, photon (Poisson) noise, 1/f noise, etc.&lt;br&gt;
&lt;br&gt;
The other type of noise is spatial noise.  To calculate this you can&lt;br&gt;
take several shots of a uniform scene (this is not easy).  To&lt;br&gt;
distinguish this from temporal noise, you need to determine that the&lt;br&gt;
noise you see is really due to spatial characteristics and not normal&lt;br&gt;
pixel-to-pixel noise caused by temporal sources (like I mentioned in&lt;br&gt;
the first paragraph).  So again, you can average together many many&lt;br&gt;
frames so that you are sure all the noise (differences between pixel&lt;br&gt;
signal values) is not due to temporal reasons.  So now that you have&lt;br&gt;
this image, you can see how much the signal varies from pixel to&lt;br&gt;
pixel.  For example you can take a histogram.  There are many reasons&lt;br&gt;
why you won't have a uniform image.  Probably the two that have the&lt;br&gt;
biggest effect are vignetting and fixed pattern noise.  For&lt;br&gt;
vignetting, I'm talking about how the image gradually gets darker as&lt;br&gt;
you go from the center of the image to the edges/corners (I'm using&lt;br&gt;
the layman's definition of vignetting rather than the true optical&lt;br&gt;
engineer's definition but let's not get into that).  And you might not&lt;br&gt;
even choose to call vignetting "noise."  To get rid of vignetting (and&lt;br&gt;
any non-uniformity in illumination), you divide your image of your&lt;br&gt;
test subject/scene by an image of a completely uniform scene.  This is&lt;br&gt;
normal background correction.  If you need more explanation of that,&lt;br&gt;
let me know.  The other kind of noise is fixed pattern noise and is&lt;br&gt;
caused by variations in the various electrical circuits responsible&lt;br&gt;
for shifting the image off the sensor into your analog-to-digital&lt;br&gt;
converter.  There are also ways to reduce/eliminate this.  Such as,&lt;br&gt;
you can remove the lens and take an image.  By doing this you are&lt;br&gt;
guaranteed that no spatial variation on your sensor is due to lens&lt;br&gt;
vignetting or non-uniformity of illumination on your scene.&lt;br&gt;
&lt;br&gt;
There are many other kinds of defects in the image and you'd be&lt;br&gt;
surprised what goes on in a digital camera to "fix up" the image&lt;br&gt;
before you get it.  There's a birefringent crystal over the sensor to&lt;br&gt;
blur the image to reduce aliasing, there's an infrared filter, they&lt;br&gt;
demosaic the image to get co-site RGB pixel values where you don't&lt;br&gt;
otherwise have them (due to Bayer color filter), there's dark noise&lt;br&gt;
(dark current) removal, there's a median filter to remove dead pixels,&lt;br&gt;
etc. and all that happens even if you are dealing with the "Raw"&lt;br&gt;
image.  Then they apply other "fixes" which sometimes you can&lt;br&gt;
optionally turn off, such as applying a gamma to lighten dark areas,&lt;br&gt;
applying edge enhancement ("sharpening"), color enhancement, as well&lt;br&gt;
as proprietary algorithms such as automatic red eye removal, automatic&lt;br&gt;
face detection, etc. SPIE has offered a day long course on the subject&lt;br&gt;
at the Electronic Imaging symposium before.&lt;br&gt;
&lt;br&gt;
For your project, I'd probably describe some or most of these noise&lt;br&gt;
sources, then measure the temporal noise by snapping a bunch of&lt;br&gt;
pictures of a constant, uniform scene (a white piece of paper), and&lt;br&gt;
then measure the fixed pattern noise by taking off the lens (if you&lt;br&gt;
can) and again average a bunch of frames and find the mean and&lt;br&gt;
standard deviation of the average image.  You can then enhance the&lt;br&gt;
contrast of the average image so that the fixed pattern noise will be&lt;br&gt;
readily apparent in your report.  You should see something like&lt;br&gt;
stripes/streaks in one direction.  I would think that should make a&lt;br&gt;
fairly decent senior year report.  You could actually do it all in a&lt;br&gt;
day (for picture snapping and image analysis) plus another day to&lt;br&gt;
write it all up.&lt;br&gt;
Good luck,&lt;br&gt;
ImageAnalyst&lt;br&gt;
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