Signal Resilient to Interpolation

The calculation of the intensity-curvature terms is related to the discrete sample of signal values and is also related to the analog/continuous transformation necessary to the function to become an interpolator. Such calculation is undertaken within the entire spatial extent of the sampling location, and as shown in Ciulla (2009), leads to the measurement of the energy level change determined through the interpolation function which is called: the Intensity-Curvature Functional. The idea is therefore to equate the intensity-curvature term calculated with the signal (image) through the given mathematical function in two conditions: (i) the given signal as it has been sampled and (ii) the signal calculated at locations where is unknown because of the limitations of the sampling instrument. Through mathematical developments that illustrate concepts of algebra and calculus, the equation of the two intensity-curvature terms furnish the instrument apt for deriving a new signal. This new signal is dependent on the given model function and is called Signal Resilient to Interpolation (SRI). The book includes illustration of the math processes, logical reasoning, which show how to generate the Signal Resilient to Interpolation. Within the book, the Signal Resilient to Interpolation is derived on the basis of quadratic and cubic polynomials in one, two and three dimensions, embedding and not embedding the pixel (voxel in three dimensions) to be re-sampled. Once the signal is modeled through an interpolator, it is possible to calculate the second order derivatives, and thus the curvature of the model interpolator which is nonetheless the modeled representation of the curvature of the signal. Geometrically, the curvature of the signal is the tangent to the first order derivative curve of the signal. There are two types of curvature treated in this book and they are: (i) classic-curvature and (ii) resilient curvature.