HighOrder Distortions

[Oliver-Loeffler]

Add support for image distortions of higher (polynomial) order.

Requirements:

• support for 2nd order including cross-terms kn*X*Y, 2nd order means up to km*X^2*Y^2
• model shall be parametrical, so that each coefficient can be toggled independently (e.g. equation such as t + a*X + b*Y + c*X^2 + d*X*Y + e*X^2*Y + f * X*Y^2 + g*X^2 * Y^2 etc.). Each coefficient in {a,b,c,d,e,f,g} should be independently configured to be enabled/disabled.
• Ideally a builder is there to create the configuration.
• The model should determine best fit conditions independently depending on data Orientation (Dimension) and SpatialDistribution.
• Ideally, model should be expandable to higher orders beyond 2nd order - but right now I have no good idea how to do that.

[Oliver-Loeffler]

Idea:

• Either create an enum type for coefficients so that each coefficient can have a name etc. OR just number the coefficients, so coefficients will be int. If coefficients are int, then lets say that all odd int values denote coefficients for x-axis and all even int values will be used as y-axis coefficient indices.

• Or, do not distinguish between X/Y axis in coefficient names actually its not needed.

• Create a HigherOrderEquation, one for X, one for Y, pass the selected coefficients into the equation and if queried, the equation will provide coefficients with their values.