Image noise reduction using spectral transforms

Patent metadata

Field	Value
Publication number	US-11227365-B2
Application number	US-201916689897-A
Country	US
Kind code	B2
Filing date	Nov 20, 2019
Priority date	Jan 3, 2017
Publication date	Jan 18, 2022
Grant date	Jan 18, 2022

Abstract

Various techniques are provided for reducing noise in captured image frames. In one example, a method includes determining row values for image frames comprising scene information and noise information. The method also includes performing first spectral transforms in a first domain on corresponding subsets of the row values to determine first spectral coefficients. The method also includes performing second spectral transforms in a second domain on corresponding subsets of the first spectral coefficients to determine second spectral coefficients. The method also includes selectively adjusting the second spectral coefficients. The method also includes determining row correction terms based on the adjusted second spectral coefficients to reduce the noise information of the image frames. Additional methods and systems are also provided.

First claim

What is claimed is:

1. A method comprising:
determining a plurality of column mean values for an image frame comprising scene information and noise information;
selecting a plurality of partitions of the image frame, wherein each partition comprises a plurality of pixels of the image frame;
processing each of the partitions by:
performing a spectral transform on at least a subset of the column mean values to determine spectral coefficients,
adjusting the spectral coefficients to reduce the scene information, and
performing a reverse spectral transform on the adjusted spectral coefficients to determine adjusted column mean values; and

selectively updating column correction terms for the image frame to reduce the noise information using the adjusted column mean values separately determined for each of the partitions.

2. The method of claim 1 , further comprising:
for each column of the image frame, determining a standard deviation for the adjusted column mean values of the partitions;
comparing the standard deviations to a threshold; and
wherein the updating comprises updating the column correction terms associated with the standard deviations less than the threshold.

3. The method of claim 2 , wherein each of the updated column correction terms comprises an average of the adjusted column mean values of the partitions associated with the column of the image frame.

4. The method of claim 1 , wherein the adjusting comprises reducing at least one of the spectral coefficients.

5. The method of claim 4 , wherein the one of the spectral coefficients is associated with a basis function component having a lowest frequency among the spectral coefficients.

6. The method of claim 1 , wherein the column mean values comprise a one dimensional array.

7. The method of claim 6 , further comprising performing the spectral transform, the adjusting, and the reverse spectral transform to determine adjusted column mean values for a plurality of overlapping subsets of the column mean values of the one dimensional array.

8. The method of claim 1 , wherein the spectral transform is a discrete cosine transform or a discrete wavelet transform.

9. The method of claim 1 , further comprising applying the correction terms to the image frame to reduce the noise information.

10. A system comprising:
a memory component storing machine-executable instructions; and
a processor configured to execute the instructions to cause the system to:
determine a plurality of column mean values for an image frame comprising scene information and noise information,
select a plurality of partitions of the image frame, wherein each of the partitions comprises a plurality of pixels of the image frame;
process each of the partitions to:
perform a spectral transform on at least a subset of the column mean values to determine spectral coefficients,
perform an adjustment of the spectral coefficients to reduce the scene information and,
perform a reverse spectral transform on the adjusted spectral coefficients to determine adjusted column mean values, and

selectively update column correction terms for the image frame to reduce the noise information using the adjusted column mean values separately determined for each of the partitions.

11. The system of claim 10 , wherein the processor is configured to execute the instructions to cause the system to:
for each column of the image frame, determine a standard deviation for the adjusted column mean values of the partitions;
compare the standard deviations to a threshold; and
wherein the updates comprise updates of the column correction terms associated with the standard deviations less than the threshold.

12. The system of claim 11 , wherein each of the updated column correction terms comprises an average of the adjusted column mean values of the partitions associated with the column of the image frame.

13. The system of claim 10 , wherein the adjustment of the spectral coefficients comprises a reduction of at least one of the spectral coefficients.

14. The system of claim 13 , wherein the one of the spectral coefficients is associated with a basis function component having a lowest frequency among the spectral coefficients.

15. The system of claim 10 , wherein the column mean values comprise a one dimensional array.

16. The system of claim 15 , wherein the processor is configured to execute the instructions to cause the system to perform the spectral transform, the adjustment, and the reverse spectral transform to determine adjusted column mean values for a plurality of overlapping subsets of the column mean values of the one dimensional array.

17. The system of claim 10 , wherein the spectral transform is a discrete cosine transform or a discrete wavelet transform.

18. The system of claim 10 , wherein the processor is configured to execute the instructions to cause the system to apply the correction terms to the image frame to reduce the noise information.

Assignees

• Flir Systems

Inventors

• Lin Stephanie
• Högasten Nicholas

Classifications

• G06T2207/10016

  Video; Image sequence · CPC title

• G06T7/37

  using transform domain methods · CPC title

• G06T2207/10048

  Infrared image · CPC title

• G06T2207/20182

  Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering · CPC title

• G06T5/10

  using non-spatial domain filtering · CPC title