Image processing method and device

Patent metadata

Field	Value
Publication number	US-10769795-B2
Application number	US-201916719474-A
Country	US
Kind code	B2
Filing date	Dec 18, 2019
Priority date	Jan 25, 2016
Publication date	Sep 8, 2020
Grant date	Sep 8, 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 su b-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 using a plurality of different interference factor removal techniques to obtain a plurality of sample images, wherein each of the plurality of sample images is associated with a particular interference factor removal technique;
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 associated with different attributes into a target image.

2. The computer-implemented method of claim 1 , 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.

3. The computer-implemented method of claim 2 , 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.

4. The computer-implemented method of claim 2 , 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.

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 at least one of the plurality of different interference factor removal techniques includes removal of the one or more interference factors using a particular 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 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.

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 using a plurality of different interference factor removal techniques to obtain a plurality of sample images, wherein each of the plurality of sample images is associated with a particular interference factor removal technique;
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 associated with different attributes 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 at least one of the plurality of different interference factor removal techniques includes removal of the one or more interference factors 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 computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-tra

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

• G06T3/4038 Primary

  Image mosaicing, e.g. composing plane images from plane sub-images · CPC title