Inverse B-spline interpolation

Nearest neighbor and beyond

The two simplest forms of the forward interpolation operators are the 1-point nearest neighbor interpolation with the weight

$$W (x, n) = \left\{\begin{array}{lcr} 1, & \mbox{for} & n - ... ...leq x < n + 1/2 \\ 0, & \mbox{otherwise} & \end{array}\right.$$ (2)

and the 2-point linear interpolation with the weight

$$W (x, n) = \left\{\begin{array}{lcr} 1 - \vert x-n\vert, & ... ... \leq x < n + 1 \\ 0, & \mbox{otherwise} & \end{array}\right.$$ (3)

Because of their simplicity, the nearest neighbor and linear interpolation methods are very practical and easy to apply. Their accuracy is, however, limited and may be inadequate for interpolating high-frequency signals. The shapes of interpolants (2) and (3) and their spectra are plotted in Figures 1 and 2. The spectra plots show that both interpolants act as low-pass filters, preventing the high-frequency energy from being correctly interpolated.

nnint
Figure 1. Nearest neighbor interpolant (left) and its spectrum (right).

linint
Figure 2. Linear interpolant (left) and its spectrum (right).

On the other side of the accuracy scale, there is the infinitely long sinc interpolant:

$$W (x, n) = \frac{\sin \left[\pi (x - n) \right]}{\pi (x - n)} \;.$$ (4)

According to the sampling theorem (Shannon, 1949; Kotel'nikov, 1933), equation (4) provides an optimal interpolation for any band-limited signal. In practice, it is not directly applicable because of a prohibitively expensive computation. The shape of the sinc function and its spectrum are shown in Figure 3. The spectrum is identically equal to one in the Nyquist frequency band.

sincint
Figure 3. Sinc interpolant (left) and its spectrum (right).

Several approaches exist for extending the nearest neighbor and linear interpolation to more accurate (albeit more expensive) methods. One example is the 4-point cubic convolution suggested by Keys (1981). The cubic convolution interpolant is a local piece-wise cubic function, which approximates the ideal sinc equation (4). Another popular approach is to taper the ideal sinc function in a local window. For example, one can use the Kaiser window (Kaiser and Shafer, 1980)

$$W (x, n) = \left\{\begin{array}{lcr} \displaystyle \frac... ... N < x < n + N \\ 0, & \mbox{otherwise} & \end{array}\right.$$ (5)

where $I_0$ is the zero-order modified Bessel function of the first kind. The Kaiser-windowed sinc interpolant (5) has the adjustable parameter $\alpha$, which controls the behavior of its spectrum. I have found empirically the value of $\alpha=4$ to provide a spectrum that deviates from 1 by no more than 1% in a relatively wide band.

I compare the accuracy of different forward interpolation methods on a one-dimensional signal shown in Figure 4. The ideal signal has an exponential amplitude decay and a quadratic frequency increase from the center towards the edges. It is sampled at a regular 50-point grid and interpolated to 500 regularly sampled locations. The interpolation result is compared with the ideal one. Observing Figures 5, 6, and 7, we can see the interpolation error steadily decreasing as we go subsequently from 1-point nearest neighbor to 2-point linear, 4-point cubic convolution, and 8-point windowed sinc interpolation. At the same time, the cost of interpolation grows proportionally to the interpolant length.

chirp Figure 4. One-dimensional test signal. Top: ideal. Bottom: sampled at 50 regularly spaced points. The bottom plot is the input in a forward interpolation test.

binlin Figure 5. Interpolation error of the nearest neighbor interpolant (dashed line) compared to that of the linear interpolant (solid line).

lincub Figure 6. Interpolation error of the linear interpolant (dashed line) compared to that of the cubic convolution interpolant (solid line).

cubkai Figure 7. Interpolation error of the cubic convolution interpolant (dashed line) compared to that of the 8-point windowed sinc interpolant (solid line).

The differences among different methods are also clearly visible from the discrete spectra of the corresponding interpolants. The left plots in figures 8 and 9 show discrete interpolation responses: the function $W(x,n)$ for a fixed value of $x=0.7$. The right plots compare the corresponding discrete spectra. We can see that the spectrum gets flatter and wider as the accuracy of the method increases.

speclincub Figure 8. Discrete interpolation responses of linear and cubic convolution interpolants (left) and their discrete spectra (right) for $x=0.7$.

speccubkai Figure 9. Discrete interpolation responses of cubic convolution and 8-point windowed sinc interpolants (left) and their discrete spectra (right) for $x=0.7$.

Inverse B-spline interpolation