Interpolation is a technique for adding new data points within a range of a set of known data points. You can use interpolation to fill-in missing data, smooth existing data, make predictions, and more. Interpolation in MATLAB® is divided into techniques for data points on a grid and scattered data points.
|1-D data interpolation (table lookup)|
|Interpolation for 2-D gridded data in meshgrid format|
|Interpolation for 3-D gridded data in meshgrid format|
|Interpolation for 1-D, 2-D, 3-D, and N-D gridded data in ndgrid format|
|Gridded data interpolation|
|Piecewise Cubic Hermite Interpolating Polynomial (PCHIP)|
|Cubic spline data interpolation|
|Evaluate piecewise polynomial|
|Make piecewise polynomial|
|Extract piecewise polynomial details|
|Padé approximation of time delays|
|1-D interpolation (FFT method)|
Introduction to interpolating gridded and scattered data sets.
Interpolation of regularly spaced, axis-aligned data sets.
This example shows how to interpolate three 1-D data sets in a single pass using
This example shows how to reduce the dimensionality of the grid plane arrays in 3-D to solve a 2-D interpolation problem.
This example shows how to use
griddedInterpolant to resample the pixels in an image.
Interpolating scattered data using
Extrapolating scattered data using
This example shows how to use normalization to improve scattered data interpolation results with
Perform nearest-neighbor and linear interpolation on a scattered set of points using a specific Delaunay triangulation.