System and method for obtaining and applying a vignette filter and grain layer

Patent metadata

Field	Value
Publication number	US-11631202-B2
Application number	US-202117389576-A
Country	US
Kind code	B2
Filing date	Jul 30, 2021
Priority date	Jan 8, 2021
Publication date	Apr 18, 2023
Grant date	Apr 18, 2023

Abstract

A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image may include obtaining the input image including at least one of the vignette effect or the grain effect; identifying at least one of a vignette parameter or a grain parameter of the input image; obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image.

First claim

What is claimed is:

1. A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image, the method comprising:
obtaining the input image including at least one of the vignette effect or the grain effect;
identifying at least one of a vignette parameter or a grain parameter of the input image;
obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and
generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image,
wherein the method further comprises:
identifying average pixel intensities of concentric radial intensity bands of the input image;
inputting the average pixel intensities of the concentric radial intensity bands into a first machine learning model; and
identifying the vignette parameter based on an output of the first machine learning model.

2. The method of claim 1 , further comprising:
multiplying the image by the vignette filter; and
generating the image based on multiplying the image by the vignette filter.

3. The method of claim 1 , further comprising:
dividing the input image into a plurality of patches;
inputting the plurality of patches into a second machine learning model; and
identifying the grain parameter based on an output of the second machine learning model.

4. The method of claim 1 , wherein the first machine learning model is trained based on a plurality of training images including different vignette strengths.

5. The method of claim 3 , wherein the second machine learning model is trained based on a plurality of training images including different grain strengths.

6. The method of claim 1 , further comprising:
generating the vignette filter or the grain layer;
storing the vignette filter or the grain layer; and
applying the vignette filter or the grain layer to another image during capture of the another image in real-time.

7. A device for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image, the device comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
obtain the input image including at least one of the vignette effect or the grain effect;
identify at least one of a vignette parameter or a grain parameter of the input image;
obtain at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and
generate the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image,
wherein the processor is further configured to:
identify average pixel intensities of concentric radial intensity bands of the input image;
input the average pixel intensities of the concentric radial intensity bands into a first machine learning model; and
identify the vignette parameter based on an output of the first machine learning model.

8. The device of claim 7 , wherein the processor is further configured to:
multiply the image by the vignette filter; and
generate the image based on multiplying the image by the vignette filter.

9. The device of claim 7 , wherein the processor is further configured to:
divide the input image into a plurality of patches;
input the plurality of patches into a second machine learning model; and
identify the grain parameter based on an output of the second machine learning model.

10. The device of claim 7 , wherein the first machine learning model is trained based on a plurality of training images including different vignette strengths.

11. The device of claim 9 , wherein the second machine learning model is trained based on a plurality of training images including different grain strengths.

12. The device of claim 7 , wherein the processor is further configured to:
generate the vignette filter or the grain layer;
store the vignette filter or the grain layer; and
apply the vignette filter or the grain layer to another image during capture of the another image in real-time.

13. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors of a device for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image, cause the one or more processors to:
obtain the input image including at least one of the vignette effect or the grain effect;
identify at least one of a vignette parameter or a grain parameter of the input image;
obtain at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and
generate the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image,
wherein the one or more instructions further cause the one or more processors to:
identify average pixel intensities of concentric radial intensity bands of the input image;
input the average pixel intensities of the concentric radial intensity bands into a first machine learning model; and
identify the vignette parameter based on an output of the first machine learning model.

14. The non-transitory computer-readable medium of claim 13 , wherein the one or more instructions further cause the one or more processors to:
multiply the image by the vignette filter; and
generate the image based on multiplying the image by the vignette filter.

15. The non-transitory computer-readable medium of claim 13 , wherein the one or more instructions further cause the one or more processors to:
divide the input image into a plurality of patches;
input the plurality of patches into a second machine learning model; and
identify the grain parameter based on an output of the second machine learning model.

16. The non-transitory computer-readable medium of claim 13 , wherein the first machine learning model is trained based on a plurality of training images including different vignette strengths.

17. The non-transitory computer-readable medium of claim 15 , wherein the second machine learning model is trained based on a plurality of training images including different grain strengths.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Abdelhamed Abdelrahman
• Brown Michael Scott
• Yim Jonghwa
• Choe Jihwan
• Kim Kihwan

Classifications

• G06T11/10 Primary

  Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) · CPC title

• G06V10/507

  Summing image-intensity values; Histogram projection analysis · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V10/774

  Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title