Episode 4, Exercise 2 Display Adjustment

### Brief description
1. Explain or revisit the image formation process:
fluorescence (analog) -> pixel values (discrete) -> color on screen (human perception)
2. Discuss human (color) perception. How many gray-values/ colors can humans distinguish?
3. How do we get from pixel-values to a perception? How are they related? In the exersice `plt.imshow` and its most important arguments are discussed.

#### Linear relations
Illustrate the relation of physical fluorescence to pixel values and pixel values to _colors_ on a screen as a function of each over, e. g.:

Similarly, examplarize the effect of changing the bounds of this _transfer function_, e.g.:

Extra, if `ipympl` is availble, write a little widget with _min_ and _max_ slider and a live image viewer.

#### Non-Linear relations
The relation from pixel-values - what we as humans perceive on the screen - can also be non-linear and designed with certain goals in mind, e.g. gamma correction

makes low pixel-values appear brighter than they would be using the linear relation. What are possible cosequences when using non-linear relations?

4. Best practices for image adjustments and viewing
5. Discuss and illustrate other _sequential_ colormaps and Show examples of _non-sequential_ colormaps and their use cases (e.g. cyclic or categorical pixel values)

### Learning objective(s)
The first [learning objective](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#display-adjustment) is covered in 1. and 2.. The second in 3. and 4.

### Volunteer(s)
@sommerc