Signal-to-noise enhancement

• US 10,643,309 B2
• Filed: 11/09/2017
• Issued: 05/05/2020
• Est. Priority Date: 11/09/2017
• Status: Active Grant

• Current Assignee: Spectral Sciences, Inc.
• Original Assignee: Spectral Sciences, Inc.
• Inventors: Adler-Golden, Steven
• Primary Examiner(s): Abdi, Amara
• Application Number:
  US15/808,063
• Publication Number:
  US 20190139196A1
• Time in Patent Office: 908 Days
• CPC Class Codes: G01J 2003/2826 Multispectral imaging, e.g. filter imaging
• CPC Class Codes: G01J 2003/2843 Processing for eliminating interfering spectra
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01J 3/2823 Imaging spectrometer
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10036 Multispectral image; Hyperspectral image
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

This disclosure relates to processing a spectral dataset, such as a hyperspectral image or a large collection of individual spectra taken with the same spectrometer, to increase the signal-to-noise ratio. The methods can also be used to process a stack of images that differ by acquisition time rather than wavelength. The methods remove most of the sensor background noise with minimal corruption of image texture, anomalous or rare spectra or waveforms, and spectral or time resolution.

Claims

1. A signal-to-noise enhancement method, comprising:

receiving from a spectrometer a spectral dataset that comprises a multiplicity of spectra measured with the spectrometer, in which the number of spectra exceeds the number of measured wavelength bands of the spectrometer;

estimating the covariance matrix of background noise associated with the measured spectra;

transforming the multiplicity of spectra to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the multiplicity of spectra onto an individual MNF eigenvector;

calculating nonlinear mathematical functions, associated with each individual MNF eigenvector, that convert the values of the MNF components of a given spectrum into estimated values of the corresponding noise-free MNF components of the given spectrum, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^p)/[r exp(−

y²)+exp(−

|y/s|^p)];

applying the inverse MNF transform to convert the estimated noise-free MNF components of the multiplicity of spectra back into the original units of the spectra; and

generating an output spectral dataset from the converted estimated noise-free MNF components of the multiplicity of spectra.

5. The method of claim 1, in which the multiplicity of spectra comprise a hyperspectral image.

2. A signal-to-noise enhancement method, comprising:

receiving from a spectrometer a spectral dataset that comprises a multiplicity of spectra measured with the spectrometer, in which the number of spectra exceeds the number of measured wavelength bands of the spectrometer;

estimating the covariance matrix of background noise associated with the measured spectra;

transforming the multiplicity of spectra to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the multiplicity of spectra onto an individual MNF eigenvector;

calculating nonlinear mathematical functions, associated with each individual MNF eigenvector, that convert the values of the MNF components of a given spectrum into estimated values of the corresponding noise-free MNF components of the given spectrum, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1];

applying the inverse MNF transform to convert the estimated noise-free MNF components of the multiplicity of spectra back into the original units of the spectra; and

generating an output spectral dataset from the converted estimated noise-free MNF components of the multiplicity of spectra.

6. The method of claim 2, in which the multiplicity of spectra comprise a hyperspectral image.

3. A system, comprising:

a spectrometer that provides a multiplicity of spectra, in which the number of spectra exceeds the number of measured wavelength bands of the spectrometer; and

digital image processing circuitry which, in operation;

estimates the covariance matrix of background noise associated with the measured spectra;

transforms the multiplicity of spectra to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the multiplicity of spectra onto an individual MNF eigenvector;

calculates nonlinear mathematical functions, associated with each individual MNF eigenvector, that convert the values of the MNF components of a given spectrum into estimated values of the corresponding noise-free MNF components of the given spectrum, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^p)/[r exp(−

y²)+exp(−

|y/s|^p)];

applies the inverse MNF transform to convert the estimated noise-free MNF components of the multiplicity of spectra back into the original units of the spectra; and

generates an output spectral dataset from the converted estimated noise-free MNF components of the multiplicity of spectra.

7. The system of claim 3, in which the spectrometer comprises a hyperspectral imaging sensor.

4. A system, comprising:

a spectrometer that provides a multiplicity of spectra, in which the number of spectra exceeds the number of measured wavelength bands of the spectrometer; and

digital image processing circuitry which, in operation;

estimates the covariance matrix of background noise associated with the measured spectra;

transforms the multiplicity of spectra to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the multiplicity of spectra onto an individual MNF eigenvector;

calculates nonlinear mathematical functions, associated with each individual MNF eigenvector, that convert the values of the MNF components of a given spectrum into estimated values of the corresponding noise-free MNF components of the given spectrum, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1];

applies the inverse MNF transform to convert the estimated noise-free MNF components of the multiplicity of spectra back into the original units of the spectra; and

generates an output spectral dataset from the converted estimated noise-free MNF components of the multiplicity of spectra.

8. The system of claim 4, in which the spectrometer comprises a hyperspectral imaging sensor.