December 30, 2014
Saturated Images
I have an interest in understanding the math behind digital image manipulations, like lightening and darkening images, converting color images to grayscale, and sharpening images. Sometimes I wonder what an image would look like if you performed a set of math operations on its pixels. Sometimes I write a computer program so I can see the results of these operations. And, on occasion, I like the results.
This image is the result of retaining each pixel's hue, but maximizing its saturation. I call this a "Saturated Image".
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Flat Color Images
What happens when photographers are also computer programmers...
OK, so here's a color photograph (of veggies, just picked from our garden, in a nice plastic bowl, sitting on my work table, by the light from a nearby window). We see photos like this all the time. Nothing special here.
We are also used to seeing black and white (or grayscale) versions of the world. Here is the color photo from above as a grayscale image. The lightness/darkness information in the original photo has been kept, but the color information has been discarded. Your iPhone can do this, as can lots of other devices and software packages. (Although there are better and worse ways of converting from color to grayscale.)
Now ask yourself: What it would look like to keep the color information and discard the lightness/darkness information instead. Well, it looks like this. The hues of the colors have been kept, but their lightness/darkness has been adjusted to a single middle value. Notice, for example, that the grain of the wood has disappeared. That's because it's the same color as the rest of the wood, only darker.
Learning how the color math worked was the real fun here; along with writing a program to apply it to digital images. But I kind of like the results, too. I call this a "Flat Color Image".
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