Color correction system and method

• US 9,742,960 B2
• Filed: 06/07/2016
• Issued: 08/22/2017
• Est. Priority Date: 05/15/2015
• Status: Active Grant

• Current Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Original Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Inventors: Chen, Wei, Cao, Zisheng
• Primary Examiner(s): Hung, Yubin
• Application Number:
  US15/176,037
• Publication Number:
  US 20170041507A1
• Time in Patent Office: 441 Days
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G09G 2320/0693 Calibration of display systems
• CPC Class Codes: G09G 2340/0407 Resolution change, inclusive of the use of different resolutions for different screen areas
• CPC Class Codes: G09G 2340/06 Colour space transformation
• CPC Class Codes: G09G 2370/12 Use of DVI or HDMI protocol in interfaces along the display data pipeline
• CPC Class Codes: G09G 2380/08 Biomedical applications
• CPC Class Codes: G09G 5/02 characterised by the way in which colour is displayed details of colour display specific for CRTs G09G1/28; specific for flat matrix panels other than liquid crystal displays G09G3/2003; specific for liquid crystal displays G09G3/3607
• CPC Class Codes: G09G 5/06 using colour palettes, e.g. look-up tables
• CPC Class Codes: H04N 1/60 Colour correction or control H04N1/54 takes precedence
• CPC Class Codes: H04N 1/6027 Correction or control of colour gradation or colour contrast H04N1/6058 takes precedence
• CPC Class Codes: H04N 1/6033 using test pattern analysis H04N1/6055 takes precedence
• CPC Class Codes: H04N 23/88 for colour balance, e.g. white-balance circuits or colour temperature control
• CPC Class Codes: H04N 9/68 for controlling the amplitude of colour signals, e.g. automatic chroma control circuits H04N9/71, H04N9/73 take precedence; camera processing pipelines for controlling the colour saturation of colour signals H04N23/86

Abstract

A system and method for calibrating a digital imaging device for color correction is disclosed. The method comprises obtaining an input color value and a reference color value for each of a plurality of color references, as well as a noise evaluation image having a color noise for evaluating noise reduction, the input color values and reference color values being in a non-linear color space. A plurality of color correction parameters are determined as optimized based on evaluating a fitness function in the non-linear color space. The non-linear color space can be a CIE L*a*b* color space. The fitness function can include a color correction error and a noise amplification metric so as to reduce noise amplification during color correction.

Claims

1. A method for calibrating a digital imaging device for color correction, comprising:

obtaining input color values and reference color values for respective color references, the input color values and reference color values being in a non-linear color space;

obtaining a noise evaluation image having a color noise for evaluating noise reduction; and

adjusting a plurality of color correction parameters based on the input color values, reference color values, and the noise evaluation image using a fitness function, having a color correction error and a noise amplification metric, wherein said adjusting comprises determining the noise amplification metric by;

color correcting the noise evaluation image using the color correction parameters; and

comparing the corrected noise evaluation image with the pre-correction noise evaluation image.

2. The method of claim 1, wherein said obtaining the input color values comprises imaging the color references each having a plurality of color patches.

3. The method of claim 1, wherein said obtaining the noise evaluation image comprises obtaining a virtual noise evaluation image.

4. The method of claim 1, wherein said adjusting comprises determining the color correction error by:

color correcting the input color values; and

comparing the corrected input color values with the reference color values.

5. The method of claim 1, wherein said determining the noise amplification metric comprises determining the noise amplification metric using a peak signal-to-noise ratio (PSNR).

6. The method of claim 5, wherein said determining the noise amplification metric comprises finding a PSNR difference that is a difference between a PSNR for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

7. The method of claim 6, wherein said determining the noise amplification metric comprises determining a downsampled PSNR difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.

8. The method of claim 1, wherein said determining the noise amplification metric comprises determining the noise amplification metric by converting the color corrected noise evaluation image into a YUV color space.

9. The method of claim 1, further comprising determining the noise amplification metric as a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

10. The method of claim 1, wherein said adjusting the color correction parameters comprises performing look-up operations and interpolation operations in a look-up table.

11. The method of claim 10, wherein said adjusting the color correction parameters comprises performing an interpolation operation that is a Shepard interpolation.

12. An apparatus, comprising:

a processor configured to;

receive input color values and reference color values for respective color references, the input color values and reference color values being in a non-linear color space;

receive a noise evaluation image having a color noise for evaluating noise reduction; and

adjust a plurality of color correction parameters based on the input color values, reference color values, and the noise evaluation image using a fitness function having a color correction error and a noise amplification metric, wherein said adjusting comprises determining the noise amplification metric by;

color correcting the noise evaluation image using the color correction parameters; and

comparing the corrected noise evaluation image with the pre-correction noise evaluation image.

13. The apparatus of claim 12, wherein said processor is configured to determine the color correction error by:

color correcting the input color values; and

comparing the corrected input color values with the reference color values.

14. The apparatus of claim 12, wherein said processor is configured to determine the noise amplification metric using a peak signal-to-noise ratio (PSNR).

15. The apparatus of claim 14, wherein said processor is configured to determine the noise amplification metric by finding a PSNR difference that is a difference between a PSNR for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

16. The apparatus of claim 15, wherein said processor is configured to determine the noise amplification metric by determining a downsampled PSNR difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.

17. The apparatus of claim 12, wherein the noise evaluation image comprises a plurality of color patches, and wherein the noise amplification metric is determined as a weighted sum of noise amplification levels of each of the color patches.

18. The apparatus of claim 12, wherein the apparatus is mounted aboard an unmanned aerial vehicle (UAV).

19. The apparatus of claims 12, wherein said determining the noise amplification metric comprises determining the noise amplification metric by converting the color corrected noise evaluation image into a YUV color space.

20. The apparatus of claims 12, wherein said adjusting the color correction parameters comprises performing look-up operations and interpolation operations in a look-up table.

21. An imaging device, comprising:

an image sensor for imaging a plurality of color references; and

a processor configured to;

receive input color values and reference color values for respective color references, the input color values and reference color values being in a non-linear color space;

receive a noise evaluation image having a color noise for evaluating noise reduction; and

adjust a plurality of color correction parameters based on the input color values, reference color values, and the noise evaluation image using a fitness having a color correction error and a noise amplification metric, wherein said adjusting comprises determining the noise amplification metric by;

color correcting the noise evaluation image using the color correction parameters; and

comparing the corrected noise evaluation image with the pre-correction noise evaluation image.

22. The imaging device of claims 21, wherein the imaging device is mounted aboard an unmanned aerial vehicle (UAV).

23. The imaging device of claims 21, wherein the processor is configured to determine the noise amplification metric as a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

24. The imaging device of claims 21, wherein said processor is configured to determine the noise amplification metric using a peak signal-to-noise ratio (PSNR).

25. A non-transitory readable medium with instructions stored thereon that, when executed by a processor, perform the steps comprising:

obtaining input color values and reference color values for respective color references, the input color values and reference color values being in a non-linear color space;

obtaining a noise evaluation image having a color noise for evaluating noise reduction; and

adjusting a plurality of color correction parameters based on the input color values, reference color values, and the noise evaluation image using a fitness having a color correction error and a noise amplification metric, wherein said adjusting comprises determining the noise amplification metric by;

color correcting the noise evaluation image using the color correction parameters; and

comparing the corrected noise evaluation image with the pre-correction noise evaluation image.

26. The non-transitory readable medium of claim 25, wherein said adjusting comprises determining the color correction error by:

color correcting the input color values; and

comparing the corrected input color values with the reference color values.

27. The non-transitory readable medium of claim 25, wherein said determining the noise amplification metric comprises determining the noise amplification metric using a peak signal-to-noise ratio (PSNR).

28. The non-transitory readable medium of claim 27, wherein said determining the noise amplification metric comprises finding a PSNR difference that is a difference between a PSNR for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

29. The non-transitory readable medium of claim 25, wherein said steps further comprise determining the noise amplification metric as a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

30. The non-transitory readable medium of claim 25, wherein said determining the noise amplification metric comprises determining a downsampled PSNR difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.