Measurement and Noise

Types of noise

Consider noise in digital photography (and find a well illustrated introduction here). I will summarize:
Noise is a deviation or error from the true picture, which is totally unrelated to the picture. Unrelated means, that one cannot predict the appearance of noise pixel if the true picture is known.

Basically, there are two types of noise on digital sensors:

  1. Noise that stems from unregularities of a particular piece of sensor. This noise is still unrelated to the picture, but it is predictable from one exposure to another.
  2. Totally random noise. Not predictable by anything.

Removing noise

It is easy to remove the totally random noise. Just take two or more exposures of the same true picture and average them. Since the noise is not correlated with the picture nor the camera it will diminish.

It is a little trickier to remove the sensor related noise. If you took several exposures of the same true picture with the same camera, the noise would also sum up and not be removed.
There are two things you can do: Substraction of the sensors noise profile or multiple exposures with different cameras.

If you choose the substractive approach, you first have to assess the sensors noise profile. How to do that? Simply take multiple exposures of different random pictures (e.g. sections of a white wall). Average the images. What remains wil be the noise profile of your sensor. You can now substract it from every future picture you make.

The alternative is to take the picture with different cameras. As the noise is only related to a specific piece of sensor, it will diminish with averaging.


“Why is this piece of blog in the category Usability Research?”

“Because there are phenomena regarding noise in usability evaluations. And we can learn from drawing the analogy.”

Up to this we can distinguish the following terms:

  • The measured object is what we want to represent as exact as possible
  • We use a certain instrument, …
  • … which produces instrument specific noise
  • Additionally we have to fight totally random noise.
  • We have the ability to make multiple independant measures with the same instrument …
  • … and multiple measures with different instruments

Multiple measures with the same instrument will remove totally random noise with the same object or will identify the instrument specific noise when different objects are measured.
Or we can measure the same object with different instruments to remove instrument related deviations.

How does this apply to usability evaluation methods? Next time!


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