Fun with imageJ

This blog is a detour or more accurately a jump-ahead from what I'd planned. When I finished the last post I'd planned to talk more about noise theory, have my buddy bring over his cameras for the measurements, post what we discovered and finally talk about imageJ and lay out how anybody, including you, my dear readers, could do the same measurements with your camera(s).

But once my last post was out in the world and I couldn't find the Badger game on TV, I downloaded the latest and greatest version of ImageJ. Once I started to play around with it I discovered some neat things to share.

ImageJ is scientific image analyser. Scientists around the world use it to pull out the data buried in the their images. Then they write their research papers, give them at confrences and publish them in journals that only scientists can understand.

Buried in my images is data on how good the Lightroom noise reduction routine is. From it I can settle which lenses to use for the YSP dress rehersals photos. Then I'll pass the pictures around in person or by email before I publish a few in this blog or on flickr. Same workflow as a research project--just more informal.

Everybody has a research project. ImageJ can be your friend. It comes free from the NIH, the National Institute of Health. So Google and download it. It's fun to play with.

In the 'What is Noise' post I talked about pixels being like penny jars. And how you filled them with photoelectrons (the pennies). And how you could measure your camera noise once you took an image of uniformly illuminated white card.

All true. But I didn't have to do all that.

Using imageJ, I loaded the blowup of Laura's head with no noise reduction. Then I dragged the yellow line down a black area. (Click the image to see it large.) With a control-K, imageJ created a noise profile from the 600 plus pixels under the line and plotted the gray scale values for me.

Gray scale images have 256 tones--255 is pure white and 0 is jet black. From these values you can calculate back to learn the number of photoelectrons in a pixel. But for this experiment I didn't need to do the calculations.

Instead I did the identical thing with the blowup of Laura after noise reduction. One glance and you can see that the noise reduction routine works. Fairly well too.

The next step is to do more comparisons to see which noise reduction programs--I have several--work the best

Once again imageJ has added new features since the last time I downloaded it. I find the free hand line profile neat and useful. In the third image, I've measured the dark background, Laura's hair and her cheek. In the hair, the noise and hair texture is mixed together. Her cheek, however, is smooth but not evenly illuminated.

The noise from the cheek is riding on the downward slope of the graph and is circled in blue. In this jpeg image the cheek noise is a good deal less than the black backgound noise. Why is a subject for another post. It's more complicated than you might think.