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How to interpolate function values in meshgrid format?
Vq = interp2(X,Y,V,Xq,Yq) returns interpolated values of a function of two variables at specific query points using linear interpolation. The results always pass through the original sampling of the function. X and Y contain the coordinates of the sample points. V contains the corresponding function values at each sample point.
How to find interpolated values on a refined grid?
Vq = interp2(V) returns the interpolated values on a refined grid formed by dividing the interval between sample values once in each dimension. Vq = interp2(V,k) returns the interpolated values on a refined grid formed by repeatedly halving the intervals k times in each dimension.
What are grid vectors and scattered points in interp2?
For interp2, grid vectors consist of a pair of vectors that define the x – and y -coordinates in a grid. The row vector defines x -coordinates, and the column vector defines y -coordinates. For interp2, scattered points consist of a pair of arrays that define a collection of points scattered in 2-D space.
How is bilinear interpolation different from other interpolations?
Unlike other interpolation techniques such as nearest-neighbor interpolation and bicubic interpolation, bilinear interpolation uses values of only the 4 nearest pixels, located in diagonal directions from a given pixel, in order to find the appropriate color intensity values of that pixel.
How to find the interpolated value of a function?
Is the interpolation based on a cubic convolution?
The interpolation is based on a cubic convolution. Modified Akima cubic Hermite interpolation. The interpolated value at a query point is based on a piecewise function of polynomials with degree at most three evaluated using the values of neighboring grid points in each respective dimension. The Akima formula is modified to avoid overshoots.
How is the interpolation of a query point based on?
The interpolated value at a query point is based on a cubic interpolation of the values at neighboring grid points in each respective dimension. The interpolation is based on a cubic spline using not-a-knot end conditions.