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Denoise signal using multiscale local 1-D polynomial transform


y = mlptdenoise(x,t)
y = mlptdenoise(x,t,numLevel)
y = mlptdenoise(___,Name,Value)
[y,T] = mlptdenoise(___)
[y,T,thresholdedCoefs] = mlptdenoise(___)
[y,T,thresholdedCoefs,originalCoefs] = mlptdenoise(___)



y = mlptdenoise(x,t) returns a denoised version of input signal x sampled at the sampling instants, t. If x or t contain NaNs, the union of the NaNs in x and t is removed before obtaining the mlpt.


y = mlptdenoise(x,t,numLevel) denoises x down to numLevel.


y = mlptdenoise(___,Name,Value) specifies mlpt properties using one or more Name,Value pair arguments, and any of the previous syntaxes

[y,T] = mlptdenoise(___) also returns the time instants for the denoised signal.

[y,T,thresholdedCoefs] = mlptdenoise(___) also returns the thresholded multiscale local 1–D polynomial transform coefficients.

[y,T,thresholdedCoefs,originalCoefs] = mlptdenoise(___) also returns the original multiscale local 1–D polynomial transform coefficients.


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Denoise a nonuniformly sampled spline signal with added noise using median smoothing and two primal vanishing moments. The nonuniformity of the signal is indicated by NaNs (missing data).

Load the data to your workspace and visualize it.

load nonuniformspline
grid on
title('Noisy Signal with Missing Data')

Denoise the data using the median denoising method.

xden = mlptdenoise(splinenoise,[],'DenoisingMethod','median');

Replace the original missing data in the correct position for plotting purposes. Visualize the original and denoised signals.

denoisedsig = NaN(size(splinenoise));
denoisedsig(~isnan(splinenoise)) = xden;
plot([splinesig denoisedsig])
grid on
legend('Original Signal','Denoised Signal');

Reduce noise of signal using the multiscale local polynomial transform (MLPT).

Load a pure sine wave signal with uniform sampling, and a corrupted version of the signal.


hold on

Use mlptdenoise to denoise the corrupted signal. Visually compare the corrupted and denoised signals against the original signal.

[xDenoised,tDenoised] = mlptdenoise(xCorrupt,tCorrupt);

hold off

Compare the error signals associated with the corrupted signal and the denoised signal. Remove NaNs from the signals for visualization purposes.

x(samplesToErase) = [];
xCorrupt(samplesToErase) = [];

xCorruptError = abs(diff([x,xCorrupt],[],2));
yError = abs(diff([x,xDenoised],[],2));

hold on
title('Error Signals')
hold off

By default, mlptdenoise denoises a signal based on the two highest-level detail coefficients. In this example, you denoise a signal to different levels and visualize the effect.

Create a multitone signal.

fs = 1000;
t = 0:1/fs:1;
x = sin(4*pi*t) + sin(120*pi*t) + sin(480*pi*t);

Denoise the signal to levels one, two, and five.

y1 = mlptdenoise(x,t,1);
y2 = mlptdenoise(x,t,2);
y5 = mlptdenoise(x,t,5);

Visualize the effect of level on the denoised signal.

title('Original Signal')

title('Denoised Signal, Level = 1')

title('Denoised Signal, Level = 2')

title('Denoised Signal, Level = 5')

The mlptdenoise function performs the forward MLPT, thresholds the coefficients as specified by the 'DenoisingMethod' name-value pair. Then mlptdenoise performs the inverse MLPT to return a denoised signal in the domain of your original signal.

You can optionally return the thresholded and original coefficients for inspection and analysis.

Denoise a nonuniformly sampled signal using Stein's unbiased risk method. Return the denoised signal, the associated time instants, the thresholded MLPT coefficients, and the original MLPT coefficients. Plot the original and denoised signals.

load nonuniformheavisine;

[xDenoised,t,wThrolded,wOriginal] = mlptdenoise(x,t,3,'denoisingmethod','SURE');

legend('Original signal','Denoised signal')

Plot the original MLPT coefficients and the thresholded MLPT coefficients for comparison.

legend('Original coefficients','Thresholded coefficients')

Input Arguments

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Input signal, specified as a vector or matrix.

  • matrix — x must have at least two rows. mlpt operates independently on each column of x. The number of elements in t must equal the row dimension of x. Any NaNs in the columns of x must occur in the same rows.

  • vector — x and t must have the same number of elements.

Data Types: double

Sampling instants corresponding to the input signal, specified as a vector, duration array, or datetime array of monotonically increasing real values. The default value depends on the length of the input signal, x.

Data Types: double | duration | datetime

Number of resolution levels, specified as a positive integer. The maximum value of numLevel depends on the shape of the input signal, x:

  • matrix — floor(log2(size(x,1)))

  • vector — floor(log2(length(x)))

mlptdenoise denoises x by thresholding all detail coefficients of an MLPT calculated for numLevel resolution levels.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'DualMoments',3 computes a transform using three dual vanishing moments.

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Number of dual vanishing moments in the lifting scheme, specified as the comma-separated pair consisting of 'DualMoments' and 2, 3 or 4.

Data Types: double

Number of primal vanishing moments in the lifting scheme, specified as the comma-separated pair consisting of 'PrimalMoments' and 2, 3, or 4.

Data Types: double

Prefilter before mlpt operation, specified as the comma-separated pair consisting of'Prefilter' and 'Haar' or 'UnbalancedHaar'. If no prefilter is specified, 'Haar' is used by default.

Data Types: char | string

Denoising method applied to MLPT detail coefficients, specified as the comma-separated pair consisting of 'DenoisingMethod' and 'Bayesian', 'Median', 'SURE', or 'FDR'.


'FDR' has an optional argument for the Q-value. Q is the proportion of false positives and is specified as a real-valued scalar between zero and one. To specify 'FDR' with a Q-value, use a cell array, where the second element is the Q-value, for example 'DenoisingMethod',{'FDR',0.01}. If unspecified, Q defaults to 0.05.

Data Types: char | string

Output Arguments

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Denoised version of the input signal, returned as a vector or matrix. The size of y depends on the size of x and the union of NaNs in x and t.

By default, the mlpt is denoised based on the two highest resolution detail coefficients, unless x has fewer than four samples. If x has fewer than four samples, the mlpt is denoised based only on the highest resolution detail coefficients.

Data Types: double

Sampling instants corresponding to the output, returned as a vector or duration array obtained from x and the input t. If the input t is a datetime or duration array, t is converted to units that enable stable mlpt and implt computation. Then T is returned as a duration array.

Data Types: double | duration

Thresholded MLPT coefficients, returned as a vector or matrix. The size of thresholdedCoefs depends on the size of x and the level to which the transform is calculated.

Data Types: double

Original MLPT coefficients, returned as a vector or matrix. The size of originalCoefs depends on the size of x and the level to which the transform is calculated.

Data Types: double


Maarten Jansen developed the theoretical foundation of the multiscale local polynomial transform (MLPT) and algorithms for its efficient computation [1][2][3]. The MLPT uses a lifting scheme, wherein a kernel function smooths fine-scale coefficients with a given bandwidth to obtain the coarser resolution coefficients. The mlpt function uses only local polynomial interpolation, but the technique developed by Jansen is more general and admits many other kernel types with adjustable bandwidths [2].


[1] Jansen, M. "Multiscale Local Polynomial Smoothing in a Lifted Pyramid for Non-Equispaced Data". IEEE Transactions on Signal Processing, Vol. 61, Number 3, 2013, pp. 545–555.

[2] Jansen, M., and M. Amghar. "Multiscale local polynomial decompositions using bandwidths as scales". Statistics and Computing (forthcoming). 2016.

[3] Jansen, M., and Patrick Oonincx. Second Generation Wavelets and Applications. London: Springer, 2005.

Introduced in R2017a

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