Color correction system and method

• US 10,244,146 B2
• Filed: 05/11/2018
• Issued: 03/26/2019
• 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:
  US15/977,661
• Publication Number:
  US 20180262650A1
• Time in Patent Office: 319 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 23/86 for controlling the colour saturation of colour signals, e.g. automatic chroma control circuits
• 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 computer-implemented method for color correction includes obtaining a noise evaluation image by adding noise to a noise-free image, color correcting the noise evaluation image using a plurality of color correction parameters, color correcting the noise-free image using the plurality of color correction parameters, determining a noise amplification metric at least by comparing the corrected noise evaluation image with the corrected noise-free image, and adjusting the plurality of color correction parameters based on the noise amplification metric.

Claims

1. A computer-implemented method for color correction, comprising:

obtaining a noise evaluation image by adding noise to a noise-free image;

color correcting the noise evaluation image using a plurality of color correction parameters;

color correcting the noise-free image using the plurality of color correction parameters;

determining a noise amplification metric at least by comparing the corrected noise evaluation image with the corrected noise-free image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

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

3. The method of claim 2, wherein determining the noise amplification metric comprises determining a PSNR for the noise evaluation image by comparing the noise evaluation image with the noise-free image, and determining a PSNR for the corrected noise evaluation image by comparing the corrected noise evaluation image with the corrected noise-free image.

4. The method of claim 3, wherein determining the noise amplification metric further comprises determining a PSNR difference that is a difference between the PSNR for the noise evaluation image and the PSNR for the corrected noise evaluation image.

5. The method of claim 4, wherein determining the noise amplification metric comprises determining a downsampled PSNR difference by:

downsampling the noise evaluation image and the noise-free image to obtain a downsampled noise evaluation image and a downsampled noise-free image;

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

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

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

7. The method of claim 1, wherein said adjusting comprises determining a fitness function using the noise amplification metric and a color correction error.

8. The method of claim 7, wherein the color correction error is determined by:

color correcting input color values; and

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

9. A system, comprising:

a memory that stores one or more computer-executable instructions; and

one or more processors configured to access the memory and execute the computer-executable instructions to perform a method comprising;

obtaining a noise evaluation image by adding noise to a noise-free image;

color correcting the noise evaluation image using a plurality of color correction parameters;

color correcting the noise-free image using the plurality of color correction parameters;

determining a noise amplification metric at least by comparing the corrected noise evaluation image with the corrected noise-free image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

10. The system of claim 9, wherein said determining the noise amplification metric comprises determining the noise amplification metric using at least one peak signal-to-noise ratio (PSNR).

11. The system of claim 10, wherein determining the noise amplification metric comprises determining a PSNR for the noise evaluation image by comparing the noise evaluation image with the noise-free image, and determining a PSNR for the corrected noise evaluation image by comparing the corrected noise evaluation image with the corrected noise-free image.

12. The system of claim 11, wherein determining the noise amplification metric further comprises determining a PSNR difference that is a difference between the PSNR for the noise evaluation image and the PSNR for the corrected noise evaluation image.

13. The system of claim 12, wherein determining the noise amplification metric comprises determining a downsampled PSNR difference by:

downsampling the noise evaluation image and the noise-free image to obtain a downsampled noise evaluation image and a downsampled noise-free image;

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

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

14. The system of claim 9, wherein said obtaining the noise evaluation image comprises obtaining a virtual noise evaluation image.

15. The system of claim 9, wherein said adjusting comprises determining a fitness function using the noise amplification metric and a color correction error.

16. The system of claim 15, wherein the color correction error is determined by:

color correcting input color values; and

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

17. One or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed by a computing system, configure the computing system to perform a method comprising:

obtaining a noise evaluation image by adding noise to a noise-free image;

color correcting the noise evaluation image using a plurality of color correction parameters;

color correcting the noise-free image using the plurality of color correction parameters;

determining a noise amplification metric at least by comparing the corrected noise evaluation image with the corrected noise-free image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

18. The one or more non-transitory computer-readable storage media of claim 17, wherein said determining the noise amplification metric comprises determining the noise amplification metric using at least one peak signal-to-noise ratio (PSNR).

19. The one or more non-transitory computer-readable storage media of claim 18, wherein determining the noise amplification metric comprises determining a PSNR for the noise evaluation image by comparing the noise evaluation image with the noise-free image, and determining a PSNR for the corrected noise evaluation image by comparing the corrected noise evaluation image with the corrected noise-free image.

20. The one or more non-transitory computer-readable storage media of claim 19, wherein determining the noise amplification metric further comprises determining a PSNR difference that is a difference between the PSNR for the noise evaluation image and the PSNR for the corrected noise evaluation image.