As we have developed topics in this blog, we are slowly moving into a very quantitative realm.  So for all of the serious techno-geeks reading this I’d like to give you a gift, or rather pass on a gift.  The National Institutes of Health has developed a very quantitative, very complete, and very open programing platgform called ImageJ that is available for download.  Did I mention that it is free?

ImageJ is not going to substitute for you artistic image processing software.  It isn’t meant to so .  But it does enable you to do some pretty sophisticated, quantitative image processing tasks.  Just as a simple example.  Consider the image of our Maine Surfer again (see Figure 1).  Using ImageJ, we draw a yellow line through the surface and that ask it to display the intensity values along this line.  This kind of thing will become very handy as we examine issues like image resolution further.

Figure 1 – Some simple image analysis performed woth freeware ImageJ

Figure 1 – Black and white image of a surfer in Maine

Figure 2 – Solarizing response curve or LUT for Figure 1

Recognize, that if we are prepared to play loosie goosie with the definition, the last image of yesterday’s post is indeed a solarization by virtue of the reversal in the tones at higher intensity.  The effect, is subtle, however.

Today I’d like to have a little fun and make a more dramatic solarization.  The steps are identical to what we did yesterday.  Let’s start with another high contrast image.  This is a silhouette that I took of a surfer last month in Kennebunport, ME.  Figure 1 shows this as a black and white image.  We again go to curves, see Figure 2 and apply a very non-linear, non-monotonic response curve or LUT.  The effect is a very surreal and solarized image.  It looks almost like a negative!

Figure 3 – Solarized variant of Figure 1 obtained by applying response curve or LUT of Figure 2

Figure 4 – Solarizing RGB response curves or LUTs

Now we can have some real fun by doing this in color.  We return to the colored version of Figure 1.  Then we apply a non-linear, non-monotonic response curve to each of the three color planes, Red, Green, and Blue.  This is shown in Figure 3.

I then cropped a bit and adjusted the brightness and contrast to make the image more pleasing and voila, we have a solarized color image.  Finally, note the way that the surfer is outlined brightly.  This effect is classic solarization.

Figure 5 – Final version – solarized color image of Maine surfer

Let’s continue our discussion of the Look Up Table or LUT. As the pixels of your digital camera’s sensor are hit by light, they emit and store electric charge – the more light, the more charge.  After the exposure, this charge is sequentially read out.  The charge is read as a voltage and the camera’s circuitry applies this voltage to what is referred to as an “analog to digital converter” or A/D.  The important point is that if you have an 8-bit camera, it converts the voltage to 2^8 or 256 grey levels for each color and for a 14-bit camera to 2^14 or 16384 grey levels for each color.  That’s your basic image.

Figure 1 – Valley with Sun, B&W image

Your image processor, e.g. Photoshop, can alter this fundamental image by applying a Look Up Table to it.  To see what this means, let’s consider the image in Figure 1.  You can guess what I was trying to accomplish with this image.  There was the valley of trees, a very overcast sky, and the sun burning through.  The problem is that there was too much dynamic range in the scene for the camera’s sensor to handle.  Probably the picture should have been shot with a graded neutral density filter or taken at a set of bracketed exposures and then reassembled as a high dynamic range image. But nevertheless, errors can be educational.

Figure 2 – Initial Photoshop CS3 histogram and response curve for Figure 1

In Figure 2, I display the Photoshop Curves screen.  Look first at the histogram.  A histogram is a display of the frequency or number of times a particular grey level appears in the image.  You can see that there are really two distinct regions: the dark tones of the valley and the high tones of the sky.

Next look at the curve, which is here a straight line.  The curve is a display of the LUT.  For a perfectly one-to-one linear LUT, like this, it says: a grey level of 0 should be displayed as 0, a grey level of 1 as 1, of 2 as 2, etc. So you can think of a two column table.  The first column is input value.  The second column is output value.  That’s your LUT.

Figure 3 – Modified response curve for Figure 1

Now this picture calls for a different curve or LUT.  I set an anchor point at the end of the first subregion of the histogram and at the beginning of the second.  The lower values I don’t change by very much – just a bit of a concavity to the curve.  But the high values I seriously distort.  In fact, as seen in Figure 3, I drive the grey levels of clouds from very bright to very dark.  This is a very nonlinear and nonmonotonic (output grey level does not always increase with increasing input grey level) curve or LUT.

The final effect is illustrated in Figure 4.  It is certainly improved in comparison to the original, but there is still a lot of work remaining to achieve my concept of the image, when I first took it.  Indeed, it may not be salvageable.  However, it does serve to illustrate the concept of the LUT and response curve.

Figure 4 – The final result of applying the response curve of Figure 3 to the image of Figure 1

I am not personally a great fan of extreme “Photoshopping.”  For my generation the rock star we emulated was Ansel Adams.  So the goal has been to achieve the ideal of his “range of light.”  This translates to a few basic techniques:

1. Spread the dynamic range from black to white – a process referred to as, in image processing parlance, as histogram equalization,
2. Adjust the gamma to get the tonal range you want,
3. Dodge and burn judiciously to accentuate the wanted and suppress the unwanted,
4. And in some cases to tone subtlety.

Radiant Tiddles, copyright 2005

But all this talk about “faking it” brought to mind a photograph that I took and played with in October of 2005.  I called this, in honor, sorry honour, of an English friend “Radiant Tiddles.”  This image is an example of a phenomenon known as “solarization.”  The first thought that you often have about solarization is that it is a form of photographic magic.  You will recall how the magic of photography is a theme of this blog.  And let me tell you that in the “good old days,” when creating a solarization involved flashing a print with light half way through development, it certainly felt like witchcraft.

Arguably, the most famous solarization is Minor White‘s “The Black Sun, 1955.”  This is a wonderful example, because you realize immediately what is going on.  Somehow the extreme white of the sun stopped being white and turned black – somehow the tone reversed. You’ll hear this referred to as “reciprocity failure.” If we look up solarization in Wikipedia, we learn that it “is due to halogen ions released within the halide grain by exposure diffusing to the grain surface in amounts sufficient to destroy the latent image.”  Let me say two things about that.  First, the latent image was the ultimate magic inherent in film photography, the fact that light created a memory in the film that could wait for years* to be brought out by chemistry.  Second, it’s a lot easier to understand as a digital phenomenon.

This understanding requires first an understanding of the concept of a “Look Up Table,” or “LUT.”  The LUT is very central and key to understanding much of what we do to create an image in digital media; so well worth the effort of understanding.

*And I do mean years later.  Film recovered in the Arctic in 1930 from an ill-fated balloon expedition in 1897 was successfully developed.