Creating Noise Profiles

I was asked what I use to create noise profiles. It is ImageJ'  a free cross platform Java app from NIH.  The best and ad free download site is from NIH-- http://rsbweb.nih.gov/ij/download.html

Once you have it installed  open your photo and choose your line type. Drag it across  the area of your photo where you want to measure noise.  Cntrl+K creates the graph.

To  change the Y scale of the graph go to edit, options, plot profile options

For  my measurement in the black area of this poster I picked  min 0, max 40

Once you have a flat noise profile, go to analyse, measure or hit Cntrl+M

For an accurate noise measurement signal average several. This is a square law thing so 4 measurements doubles your accuracy and 9 measurements triples it. Summarize will do the math where your Signal to Noise (S/N) is the Mean divided by the StdDev (Standard Deviation)

Is this conventional statistical definition of S/N to best way to characterize photographic signal to noise?Probably not.  Noise peaks, especially colored noise peaks, that jump out of the average noise are far more distracting than a mild increase in the average noise. So is pattern noise.  Both these noise problems will become lost in these numbers. But if you do these comparisons carefully ImageJ is a very useful tool for working out how well the various combinations of sliders and methods work for you.

Final note.  You can not save a picture of your graph directly in ImageJ. 'Save as' creates a spread sheet file, 'Clear\' clears out you mistakes and 'Rename' allows you to start a new results list without losing your previous numbers.

Again if you want to know more about noise read Emil's article,  http://theory.uchicago.edu/~ejm/pix/20d/tests/noise/noise-p2.html

Noise Reduction RT vs 'the others'

Two months or so back I had this post all planned out  The 'other' was to be my antiquated version of Adobe Camera RAW that came with Photoshop CS3. When I compared the noise on two of my images RT clearly lost with one image but tied with the second. Slightly confusing.

So I checked the RT forum  to see if there was some info I had missed. There I learnt that the noise reduction routines were being  rewritten.  I decided to wait for the revision. Then life interfered with blogging and I didn't come back to this until a few day ago.

mbod had already done the comparison where his 'other' was NeatImage.  The link for the discussion is http://www.rawtherapee.com/forum/viewtopic.php?f=1&t=3708  He provided this screenshot; I measured the noise in the sky with ImageJ

The interesting thing about these measurements is the noise profiles are nearly identical even though the RT processed image on the right looks much noisier. (click to view original size) That's caused by the relatively large chromatic blotches emil talk about in the forum post. They are what you see but since a line profile only measures noise that is a pixel wide, it makes the Signal /Noise look better than it really is.

Normally I do most of my photographic work on my 32 bit desktop machine. While I have 32 bit memory management problems, I also have a semi ancient Sun workstation monitor that was built to last.

And has lasted. With a large 1600 pixel screen, the monitor can be calibrated and color managed so the colors and values on the screen are identical to those I see on my prints. Just as important when I raise or lower my head, the image intensities don't change like they do on my laptop screen.

But this time I used the 64 machine after I downloaded mbod's RAW file. And I was surprised by what I saw after I ran it through RAW therapee.

The noise profile of the 64 bit jpg (left) was much less noisy than I expected. More important it didn't have the chromatic blotches that I have in my 32bit jpg (right) and that mbod had in his conversion.  (click the screen captures to view their original size). Big shock to discover there was that much difference between the two versions of RT.

     Both jpgs were converted using identical settings. The formula used was exposure compensation to correct for underexposure followed by RL sharpening with a radius of .50 and amount of .24. This was followed  by impulse noise reduction of 75 and luminous noise reduction of 49. All other settings were left at the default values.

glascort also posted a NeatImage conversion which I compared to my 64bit jpg.  While my noise is 4 times lower I lost fine detail like the guy wire on the tower (circled in red).  Larger detail like that on the side of the building (circled in blue) wasn't affected. But there is no question that my version is less crisp than the glacort's version.

That turned out to be rather easy to fix. I loaded the 64bit jpg into RT and upped the Lab mode contrast to around 30. That brought out more noise so I added a bit more impulse noise reduction. As you can see in my reworked jpg on the left, the noise is still lower than the NeatImage jpg, the overall image is as crisp and I recovered the lost guy wire (circled in red).

   While I don't have any of the 'others' installed on my 64 bit machine yet, I feel the current 4.0.6.3  64 bit noise reduction is quite good. So we might be far closer to overtaking ACR than some believe.