Image transformations and slicing
1 Image transformations
An image transformation function $T$ can be expressed as \[g(x,y)=T[f(x,y)]\] where $f(x,y)$ and $g(x,y)$ are the original and transformed images, respectively.
Typically, the operator $T$ takes neighbor pixels of $(x,y)$.
If the size of the neighborhood is $1\times 1$, $g$ is a function of $f$ only.
- Gray-level (also intensity or mapping) transformation function $s=T(r)$ where $r=f(x,y)$ and $s=g(x,y)$
- Also called point processing
1.1 Basic transformations
1.2 Image negatives
1.3 Log transformations
1.4 Power-law transformations
- Similar to log transformations, but $\gamma$ controls the overall shape of mapping curves
- Many devices use a power-law transformation
- Have you heard of gamma correction?
- Process used to correct a power-law response from the monitor
1.5 Linear contrast stretching
2 Image slicing
2.1 Gray-level slicing
Highlights a specific range of gray levels.
For example, enhancing water bodies in satellite imagery
2.2 Bit-plane slicing
Highlights contributions by specific bits.
- to identify which bit planes are significant
- to determine the number of bits required
- for image compression