Image processing method and device

Patent metadata

Field	Value
Publication number	US-10706555-B2
Application number	US-201816043636-A
Country	US
Kind code	B2
Filing date	Jul 24, 2018
Priority date	Jan 25, 2016
Publication date	Jul 7, 2020
Grant date	Jul 7, 2020

Abstract

A first image to be processed is identified, where the first image includes one or more interference factors. The one or more interference factors are removed from the first image using a plurality of different interference factor removal techniques to obtain a plurality of sample images, where each of the plurality of sample images is associated with a particular interference factor removal technique. Each sample image of the plurality of sample images is segmented into a plurality of sample sub-images based on a segmentation rule, where each sample sub-image is associated with an attribute. A plurality of target sub-images is determined from the plurality of sample sub-images, where each target sub-image comprises a combination of sample sub-images associated with a common attribute, and where each target sub-image is associated with a different attribute. The plurality of target sub-images associated with different attributes is combined into a target image.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
identifying a first image to be processed, the first image including one or more interference factors;
removing the one or more interference factors from the first image to obtain one or more sample images;
segmenting each sample image of the one or more sample images into a plurality of sample sub-images based on a segmentation rule, wherein at least one sample sub-image of the plurality of sub-images is associated with a plurality of attributes;
determining one or more target sub-images from the plurality of sample sub-images, wherein each target sub-image comprises a combination of sample sub-images associated with a common attribute among the plurality of attributes, and wherein each target sub-image is associated with a different attribute among the plurality of attributes; and
combining the one or more target sub-images into a target image.

2. The computer-implemented method of claim 1 , wherein determining the one or more target sub-images comprises, after segmented each sample image of the one or more sample images into the plurality of sample sub-images:
determining a mathematical parameter of each sample sub-image of the plurality of sample sub-images;
dividing the sample sub-images associated with the common attribute into one or more image sets based on the determined mathematical parameters, wherein each image set includes one or more sample sub-images; and
determining target sub-images from an image set that includes a maximum number of sample sub-images.

3. The computer-implemented method of claim 2 , wherein determining the mathematical parameter of each sample sub-image of the one or more sample sub-images comprises, for each sample sub-image:
generating an RGB vector for the sub-image based on RGB information of each pixel in the sample sub-image; and
identifying the RGB vector as the mathematical parameter of the sample sub-image.

4. The computer-implemented method of claim 2 , wherein the sample sub-images associated with the common attribute are divided into one or more image sets based on the determined mathematical parameters using a clustering algorithm.

5. The computer-implemented method of claim 4 , wherein the determining the target sub-image from an image set that comprises the maximum number of sample sub-images comprises determining, as the target sub-image from the image set that comprises the maximum number of sample sub-images, a sample sub-image that corresponds to the center point in the image set obtained after clustering.

6. The computer-implemented method of claim 1 , wherein the one or more interference factors include at least one of a reticulated pattern or a watermark included in the first image.

7. The computer-implemented method of claim 1 , wherein removing the one or more interference factors from the first image comprises using a plurality of interference factor removal techniques to obtain the one or more sample images, wherein each of the one or more sample images is obtained based on a corresponding interference factor removal technique of the plurality of interference factor removal techniques, and
wherein at least one of the plurality of interference factor removal techniques includes removal of the one or more interference factors using an image processing software application.

8. The computer-implemented method of claim 1 , wherein the attributes associated with a particular sample sub-image represent a location within the sample image associated with the sample image, and wherein combining the one or more target sub-images associated with different attributes into a target image comprises combining the one or more target sub-images associated with different attributes into the target image based on location coordinates of each pixel in the target sub-image.

9. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
identifying a first image to be processed, the first image including one or more interference factors;
removing the one or more interference factors from the first image to obtain a plurality of sample images;
segmenting each sample image of the plurality of sample images into a plurality of sample sub-images based on a segmentation rule, wherein each sample sub-image is associated with an attribute;
determining a plurality of target sub-images from the plurality of sample sub-images, wherein each target sub-image comprises a combination of sample sub-images associated with a common attribute, and wherein each target sub-image is associated with a different attribute; and
combining the plurality of target sub-images into a target image.

10. The non-transitory, computer-readable medium of claim 9 , wherein determining the plurality of target sub-images comprises, after segmented each sample image of the plurality of sample images into the plurality of sample sub-images:
determining a mathematical parameter of each sample sub-image of the plurality of sample sub-images;
dividing the sample sub-images associated with a common attribute into a plurality of image sets based on the determined mathematical parameters, wherein each image set includes one or more sample sub-images; and
determining target sub-images from an image set that includes a maximum number of sample sub-images.

11. The non-transitory, computer-readable medium of claim 10 , wherein determining the mathematical parameter of each sample sub-image of the plurality of sample sub-images comprises, for each sample sub-image:
generating an RGB vector for the sub-image based on RGB information of each pixel in the sample sub-image; and
identifying the RGB vector as the mathematical parameter of the sample sub-image.

12. The non-transitory, computer-readable medium of claim 10 , wherein the sample sub-images associated with a common attribute are divided into a plurality of image sets based on the determined mathematical parameters using a clustering algorithm.

13. The non-transitory, computer-readable medium of claim 12 , wherein the determining the target sub-image from an image set that comprises the maximum number of sample sub-images comprises determining, as the target sub-image from the image set that comprises the maximum number of sample sub-images, a sample sub-image that corresponds to the center point in the image set obtained after clustering.

14. The non-transitory, computer-readable medium of claim 9 , wherein the one or more interference factors include at least one of a reticulated pattern or a watermark included in the first image.

15. The non-transitory, computer-readable medium of claim 9 , wherein removing the one or more interference factors from the first image comprises using one or more interference factor removal techniques to obtain the one or more sample images, wherein at least one of the plurality of interference factor removal techniques includes using a particular image processing software application.

16. The non-transitory, computer-readable medium of claim 9 , wherein the attributes associated with a particular sample sub-image represent a location within the sample image associated with the sample image, and wherein combining the plurality of target sub-images associated with different attributes into a target image comprises combining the plurality of target sub-images associated with different attributes into the target image based on location coordinates of each pixel in the target sub-image.

17. A computer-implemented system, comprising:
one or more

Assignees

• Alibaba Group Holding Ltd

Inventors

• Lin Tong

Classifications

• G06F18/253 Primary

  of extracted features · CPC title

• G06T7/174 Primary

  involving the use of two or more images · CPC title

• G06F18/23

  Clustering techniques · CPC title

• G06T5/50

  using two or more images, e.g. averaging or subtraction · CPC title

• G06T3/40 Primary

  Scaling of whole images or parts thereof, e.g. expanding or contracting · CPC title