Signal-to-Noise Enhancement

• US 20190139196A1
• Filed: 11/09/2017
• Published: 05/09/2019
• Est. Priority Date: 11/09/2017
• Status: Active Grant

• Current Assignee: Spectral Sciences, Inc.
• Original Assignee: Spectro Incorporated (Spectro Scientific, Inc.)
• Inventors: Adler-Golden, Steven
• Granted Patent: US 10,643,309 B2
• Time in Patent Office: 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;

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.

2. The method of claim 1, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

3. The method of claim 1, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

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

5. The method of claim 4, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

6. The method of claim 4, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

7. 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;

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 7, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^p)].

9. The system of claim 7, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

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

11. The system of claim 10, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

12. The system of claim 10, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

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

receiving from an imaging device a time series of images that comprise a multiplicity of image frames, in which the number of pixels exceeds the number of image frames;

estimating the covariance matrix of the background noise associated with the images;

transforming the multiplicity of images to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the pixel waveforms 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 pixel waveform into estimated values of the corresponding noise-free MNF components of the given pixel waveform; and

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

generating an output image dataset from the converted estimated noise-free MNF components of the multiplicity of pixel waveforms.

14. The method of claim 13, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

15. The method of claim 13, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

16. The method of claim 13, in which the covariance matrix of the background noise comprises a diagonal constant matrix.

17. The method of claim 16 in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

18. The method of claim 16, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

19. A system, comprising:

a camera that provides a time series of images that comprise a multiplicity of image frames, in which the number of pixels exceeds the number of image frames; and

digital image processing circuitry which, in operation;

estimates the covariance matrix of the background noise associated with the images;

transforms the multiplicity of images to Maximum Noise Fraction (MNF) components, each component comprising the coefficients resulting from projection of the pixel waveforms 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 pixel waveform into estimated values of the corresponding noise-free MNF components of the given pixel waveform;

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

generates an output image dataset from the converted estimated noise-free MNF components of the multiplicity of pixel waveforms.

20. The system of claim 19, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

21. The system of claim 19, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].

22. The system of claim 19, in which the covariance matrix of the background noise comprises a diagonal constant matrix.

23. The system of claim 22, in which the nonlinear mathematical functions are given by S(y)=exp(−

|y/s|^P)/[r exp(−

y²)+exp(−

|y/s|^P)].

24. The system of claim 22, in which the nonlinear mathematical functions are given by S(y)=1/[r exp(−

y²)+1].