IP Library › Granted Patent US 12,439,173
Granted Patent B2
US 12,439,173 · App. 17/930,213 · Granted Oct 7, 2025

System and method of white balancing a digital image with multiple light sources

Inventors: Mahmoud Afifi (North York, CA); Michael Brown (Toronto, CA); Marcus Brubaker (Toronto, CA)
H04N23/88G06T3/40G06T7/90G06V10/82G06T2207/10024
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Quick Facts
Patent No.
US 12,439,173
App. No.
17/930,213
Granted
Oct 7, 2025
Kind
B2
Abstract

A system and method for white balancing a digital image. The method including downsampling the digital image to generate a downsampled image; processing the downsampled image with a plurality of preset white balance settings to generate a plurality of white balanced downsampled images; processing the input image at a fixed white balanced setting to produce an initial image; inputting the white balanced downsampled images to a deep neural network to generate a weighting map, the weighting map including weights of the preset white balance settings at windows of the downsampled images; generating a white balanced output image by applying the weighting map to the initial image; and outputting the white balanced output image.

Claims (30)

1. A method for white balancing an input digital image, comprising:

receiving the input digital image in an input color space;

downsampling the input image to generate a downsampled image;

processing the downsampled image with a plurality of preset white balance settings to generate a plurality of downsampled images each with a distinct target white balance;

processing the input image at a fixed white balanced setting to produce an initial image;

inputting the white balanced downsampled images to a deep neural network to generate a weighting map, the weighting map comprising weights of the preset white balance settings at windows of the downsampled images;

generating a white balanced output image by determining a sum of the weighting map applied to mapped images each with a target white balance, the mapped images each determined by minimizing a residual sum of squares between the colors of the initial image and the downsampled image with the target white balance; and

outputting the white balanced output image.

2. The method of claim 1 , wherein the preset white balance settings and the fixed white balanced setting comprise a color temperature.

3. The method of claim 2 , wherein the preset white balance settings comprise a predefined set of color temperatures or tints that each correlate to different lighting conditions.

4. The method of claim 3 , wherein generating the white balanced output image comprises linearly blending the white balanced downsampled images with the weighting map.

5. The method of claim 1 , wherein generating the white balanced output image comprises applying a polynomial kernel function on the initial image to project color channels into a higher-dimensional space.

6. The method of claim 1 , wherein the weighting map is learned from images rendered in a standard Red, Green, and Blue color space after a full rendering of demosaiced raw images.

7. The method of claim 1 , wherein the weighting map is learned from a linear raw space.

8. The method of claim 1 , wherein the deep neural network comprises a GridNet architecture.

9. The method of claim 1 , wherein the deep neural network receives a three-dimensional tensor of concatenated white balanced downsampled images and outputs a three-dimensional tensor of the weighting map.

10. A system for white balancing a digital image, the system comprising one or more processors in communication with data storage, using instructions stored on the data storage, the one or more processors are configured to execute:

an input module to receive the input digital image in an input color space;

a downsampling module to downsample the input image to generate a downsampled image;

an image processing module to process the downsampled image with a plurality of preset white balance settings to generate a plurality of downsampled images each with a distinct target white balance, to process the input image at a fixed white balanced setting to produce an initial image, and to input the white balanced downsampled images to a deep neural network to generate a weighting map, the weighting map comprising weights of the preset white balance settings at windows of the downsampled images;

a mapping module to generate a white balanced output image by determining a sum of the weighting map applied to mapped images each with a target white balance, the mapped images each determined by minimizing a residual sum of squares between the colors of the initial image and the downsampled image with the target white balance; and

an output module to output the white balanced output image.

11. The system of claim 10 , wherein the preset white balance settings and the fixed white balanced setting comprise a color temperature.

12. The system of claim 11 , wherein the preset white balance settings comprise a predefined set of color temperatures or tints that each correlate to different lighting conditions.

13. The system of claim 12 , wherein generation of the white balanced output image comprises linearly blending the white balanced downsampled images with the weighting map.

14. The system of claim 10 , wherein generation of the white balanced output image comprises applying a polynomial kernel function on the initial image to project color channels into a higher-dimensional space.

15. The system of claim 10 , wherein the weighting map is learned from images rendered in a standard Red, Green, and Blue color space after a full rendering of demosaiced raw images.

16. The system of claim 10 , wherein the weighting map is learned from a linear raw space.

17. The system of claim 10 , wherein the deep neural network comprises a GridNet architecture.

18. The system of claim 10 , wherein the deep neural network receives a three-dimensional tensor of concatenated white balanced downsampled images and outputs a three-dimensional tensor of the weighting map.

Continuity (2)
Provisional Application 63242649 · Sep 10, 2021
Related Publication 20230098058A1 · Mar 30, 2023
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