---
title: "Signal and image processing"
output: rmarkdown::html_vignette
bibliography: "references.bib"
vignette: >
%\VignetteIndexEntry{Signal and image processing}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This vignette is adapted from the official Armadillo
[documentation](https://arma.sourceforge.net/docs.html).
# One-dimensional convolution {#conv}
The `conv()` function performs a one-dimensional convolution of two vectors. The orientation of the result vector is the same as the orientation of the first input vector.
Usage:
```cpp
vec conv(x, y, shape);
```
The `shape` argument is optional and can be one of the following:
- `"full"`: return the full convolution (default setting), with the size equal to `x.n_elem + y.n_elem - 1`.
- `"same"`: return the central part of the convolution, with the same size as vector `x`.
The convolution operation is also equivalent to finite impulse response (FIR) filtering.
## Examples
```cpp
[[cpp11::register]] list conv1_(const doubles& x, const doubles& y) {
vec a = as_col(x);
vec b = as_col(y);
vec c = conv(a, b);
vec d = conv(a, b, "same");
writable::list out(2);
out[0] = as_doubles(c);
out[1] = as_doubles(d);
return out;
}
```
# Two-dimensional convolution {#conv2}
The `conv2()` function performs a two-dimensional convolution of two matrices. The orientation of the result matrix is the same as the orientation of the first input matrix.
Usage:
```cpp
mat conv2(A, B, shape);
```
The `shape` argument is optional and can be one of the following:
- `"full"`: return the full convolution (default setting), with the size equal to `size(A) + size(B) - 1`.
- `"same"`: return the central part of the convolution, with the same size as matrix `A`.
## Caveats
The implementation of 2D convolution in this version is preliminary.
## Examples
```cpp
[[cpp11::register]] list conv2_(const doubles_matrix<>& x,
const doubles_matrix<>& y) {
mat a = as_mat(x);
mat b = as_mat(y);
mat c = conv2(a, b);
mat d = conv2(a, b, "same");
writable::list out(2);
out[0] = as_doubles_matrix(c);
out[1] = as_doubles_matrix(d);
return out;
}
```
# One-dimensional Fast Fourier Transform {#fft-ifft}
The `fft()` function computes the fast Fourier transform (FFT) of a vector or matrix. The function returns a complex matrix.
Similarly, `ifft()` computes the inverse fast Fourier transform (IFFT) of a complex matrix.
The transform is done on each column vector of the input matrix.
Usage:
```cpp
// real or complex
cx_vec Y = fft(X);
cx_vec Y = fft(X, n);
// complex only
cx_mat Z = ifft(cx_mat Y);
cx_mat Z = ifft(cx_mat Y, n);
```
The optional `n` argument specifies the transform length:
- If `n` is larger than the length of the input vector, a zero-padded version of the vector is used.
- If `n` is smaller than the length of the input vector, only the first `n` elements of the vector are used.
## Caveats
* The transform is fastest when the transform length is a power of 2 ($2^k,\: k = 1, 2, 3, \ldots$).
* By default, the function uses an internal FFT algorithm based on KISS FFT. With vendoring, it is possible to
use the FFTW3 library for faster execution by modifying the cpp11armadillo header to:
```cpp
...
#include <Rmath.h>
#define ARMA_USE_FFTW3 // add this line
#include <armadillo.hpp>
...
```
## Examples
```cpp
[[cpp11::register]] list fft1_(const doubles& x) {
vec a = as_Col(x);
cx_vec b = fft(a);
cx_vec c = ifft(b);
writable::list out(2);
writable::list out2(2);
writable::list out3(2);
out2[0] = as_doubles(real(b));
out2[1] = as_doubles(imag(b));
out3[0] = as_doubles(real(c));
out3[1] = as_doubles(imag(c));
out[0] = out2;
out[1] = out3;
return out;
}
```
# Two-dimensional Fast Fourier Transform {#fft-ifft-2}
The `fft2()` function computes the two-dimensional fast Fourier transform (FFT) of a matrix. The function returns a complex matrix.
Similarly, `ifft2()` computes the inverse fast Fourier transform (IFFT) of a complex matrix.
Usage:
```cpp
// real or complex
cx_mat Y = fft2(mat X);
cx_mat Y = fft2(mat X, int n_rows, int n_cols);
// complex only
cx_mat Z = ifft2(cx_mat Y);
cx_mat Z = ifft2(cx_mat Y, int n_rows, int n_cols);
```
The optional `n_rows` and `n_cols` arguments specify the transform size:
- If `n_rows` and `n_cols` are larger than the size of the input matrix, a zero-padded version of the matrix is used.
- If `n_rows` and `n_cols` are smaller than the size of the input matrix, only the first `n_rows` and `n_cols` elements of the matrix are used.
## Caveats
- The implementation of the 2D transform in this version is preliminary.
- The transform is fastest when both `n_rows` and `n_cols` are a power of 2 ($2^k,\: k = 1, 2, 3, \ldots$).
- By default, the function uses an internal FFT algorithm based on KISS FFT. With vendoring, it is possible to
use the FFTW3 library for faster execution by modifying the cpp11armadillo header to:
```cpp
...
#include <Rmath.h>
#define ARMA_USE_FFTW3 // add this line
#include <armadillo.hpp>
...
```
## Examples
```cpp
[[cpp11::register]] list fft2_(const doubles_matrix<>& x) {
mat a = as_mat(x);
cx_mat b = fft2(a);
cx_mat c = ifft2(b);
writable::list out(2);
writable::list out2(2);
writable::list out3(2);
out2[0] = as_doubles(real(b));
out2[1] = as_doubles(imag(b));
out3[0] = as_doubles(real(c));
out3[1] = as_doubles(imag(c));
out[0] = out2;
out[1] = out3;
return out;
}
```
# One-dimensional interpolation {#interp1}
The `interp1()` function performs one-dimensional interpolation of a function specified by vectors `X` and `Y`. The function generates a vector `YI` that contains interpolated values at locations `XI`.
Usage:
```cpp
vec interp1(X, Y, XI, YI);
vec interp1(X, Y, XI, YI, method);
vec interp1(X, Y, XI, YI, method, extrapolation_value);
```
The `method` argument is optional and can be one of the following:
- `"nearest"`: interpolate using single nearest neighbour.
- `"linear"`: linear interpolation between two nearest neighbours (default setting).
- `"*nearest"`: as per `"nearest"`, but faster by assuming that `X` and `XI` are monotonically increasing.
- `"*linear"`: as per `"linear"`, but faster by assuming that `X` and `XI` are monotonically increasing.
If a location in `XI` is outside the domain of `X`, the corresponding value in `YI` is set to `extrapolation_value`.
The `extrapolation_value` argument is optional; by default, it is `datum::nan` (not-a-number).
## Examples
```cpp
[[cpp11::register]] doubles interp1_(const int& n) {
vec x = linspace<vec>(0, 3, n);
vec y = square(x);
vec xx = linspace<vec>(0, 3, 2 * n);
vec yy;
interp1(x, y, xx, yy); // use linear interpolation by default
interp1(x, y, xx, yy, "*linear"); // faster than "linear"
interp1(x, y, xx, yy, "nearest");
return as_doubles(yy);
}
```
# Two-dimensional interpolation {#interp2}
The `interp2()` function performs two-dimensional interpolation of a function specified by matrix `Z` with coordinates given by vectors `X` and `Y`. The function generates a matrix `ZI` that contains interpolated values at the coordinates given by vectors `XI` and `YI`.
Usage:
```cpp
mat interp2(X, Y, Z, XI, YI, ZI);
mat interp2(X, Y, Z, XI, YI, ZI, method);
mat interp2(X, Y, Z, XI, YI, ZI, method, extrapolation_value);
```
The `method` argument is optional and can be one of the following:
- `"nearest"`: interpolate using nearest neighbours.
- `"linear"`: linear interpolation between nearest neighbours (default setting).
If a coordinate in the 2D grid specified by `(XI, YI)` is outside the domain of the 2D grid specified by `(X, Y)`, the corresponding value in `ZI` is set to `extrapolation_value`.
The `extrapolation_value` argument is optional; by default, it is `datum::nan` (not-a-number).
## Examples
```cpp
[[cpp11::register]] doubles_matrix<> interp2_(const int& n) {
mat Z(n, n, fill::randu);
vec X = regspace(1, Z.n_cols); // X = horizontal spacing
vec Y = regspace(1, Z.n_rows); // Y = vertical spacing
vec XI = regspace(X.min(), 1.0/2.0, X.max()); // magnify by approx 2
vec YI = regspace(Y.min(), 1.0/3.0, Y.max()); // magnify by approx 3
mat ZI;
interp2(X, Y, Z, XI, YI, ZI); // use linear interpolation by default
return as_doubles_matrix(ZI);
}
```
# Find polynomial coefficients for data fitting {#polyfit}
The `polyfit()` function finds the polynomial coefficients for data fitting. The function models a 1D function specified by vectors `X` and `Y` as a polynomial of order `N` and stores the polynomial coefficients in a column vector `P`.
The given function is modelled as:
$$
y = p_0 x^N + p_1 x^{N-1} + p_2 x^{N-2} + \ldots + p_{N-1} x^1 + p_N
$$
where $p_i$ is the $i$-th polynomial coefficient. The coefficients are selected to minimise the overall error of the fit (least squares).
The column vector `P` has $N+1$ coefficients.
`N` must be smaller than the number of elements in `X`.
Usage:
```cpp
P = polyfit(X, Y, N);
polyfit(P, X, Y, N);
```
If the polynomial coefficients cannot be found:
- `P = polyfit(X, Y, N)` resets `P` and returns an error.
- `polyfit(P, X, Y, N)` resets `P` and returns a `bool` set to `false` without an error.
## Examples
```cpp
[[cpp11::register]] doubles polyfit1_(const int& n, const int& m) {
vec x = linspace<vec>(0, 1, n);
vec y = 2*pow(x,2) + 2*x + ones<vec>(n);
vec p = polyfit(x, y, m);
return as_doubles(p);
}
```
# Evaluate polynomial {#polyval}
The `polyval()` function evaluates a polynomial. Given a vector `P` of polynomial coefficients and a vector `X` containing the independent values of a 1D function, the function generates a vector `Y` that contains the corresponding dependent values.
For each `x` value in vector `X`, the corresponding `y` value in vector `Y` is generated using:
$$
y = p_0 x^N + p_1 x^{N-1} + p_2 x^{N-2} + \ldots + p_{N-1} x^1 + p_N
$$
where $p_i$ is the $i$-th polynomial coefficient in vector `P`.
`P` must contain polynomial coefficients in descending powers (e.g., generated by the `polyfit()` function).
Usage:
```cpp
Y = polyval(P, X);
```
## Examples
```cpp
[[cpp11::register]] doubles polyval1_(const int& n, const int& m) {
vec x = linspace<vec>(0, 1, n);
vec y = 2*pow(x,2) + 2*x + ones<vec>(n);
vec p = polyfit(x, y, m);
vec q = polyval(p, x);
return as_doubles(q);
}
```