Monday, 24 December 2007 01:16
We all know the situation - a picture looks unusable due to extreme
contrasts with either blown out highlights or many deep dark shadows
... or both.
There's a technique that may help to rescue such pictures: contrast
masking. Contrast masking isn't exactly new - it has been a well-known
technique with film for decades - it got much easier with with imaging
application such as Adobe Photoshop or
The Gimp (freeware). Besides some filters the imaging application
should support layers as a pre-requisite.
Ok, enough of the babbling.Here's a quite extreme sample picture which
has obviously plenty of
potential for corrections ....
The workflow for contrast masking is as follows:
You may wonder about step 4 here. Why should you blurr the "shadow"
image ? Well, try it. Without blurring you'll notice that the image
will be rather soft. By blurring the negative the details of the
original image are retained.
- make a copy of the original image
- remove all colors
- inverse the image (= B&W negative)
- Gaussian blurr the step 3 image - this is your contrast mask
- overlay the mask onto the original (as a layer - layer 1 =
original, layer 2 = mask)
- change the opacity of the mask layer down to a "sufficient"
20% may be a good start here
- adjust the levels of the resulting image to recover deep black
and bright white
Sounds complicated ? It isn't -it really isn't.
Here's the transition of the contrast mask during the workflow ...
Negative (step 3)
Let's apply the contrast mask to the original image now ...
image overlayed by 20%
with the contrast mask (step 5 + 6)
after levelling (step 7)
Let's compare the original image with the processed one ...
You will notice the improvement immediately - the shadows are much less
pronounced and the sky is a bit darker now. Nonetheless the processed
image still contains deep black as well as bright white. The difference
is also obvious in the histograms - the processed image shows a
smoothed fading towards the extreme ends of the histogram and the
primary data has been shifted towards more healthy mid-tones (to some