Basic rank filters
To understand this episode you need to know:
- Pixel properties
- Neighbourhood filters
In this module one would learn basic principles of rank image filters and understand when using this type filters might be beneficial.
- Understand how rank filters function.
- Execute and compare several rank filters.
- Compare image smoothing using rank (e.g. median) vs another filter type (e.g. mean).
graph TB pixel --> values[neighbourhood pixel values] values --> sorted[sorted pixel values] sorted --> min sorted --> max sorted --> median sorted --> ... subgraph rank value min max median ... end min --> fpixel[filtered image pixel] max --> fpixel median --> fpixel ... --> fpixel
Activity: Explore rank filters on grayscale images
- Open image: xy_8bit__two_noisy_squares_different_size.tif
- Explore how a median filter
- removes noise
- removes small structures
- preserves egdes
- Compare median filter to mean filter of same radius
True or false? Discuss with your neighbour!
- Median filter is just another name for mean filter.
- Small structures can completely disappear from an image when applying a median filter.
- median based local background subtraction