# US 10,769,795 B2 · Image processing method and device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,795 B2 - Document ID: US10769795B2 - Status: Granted - Kind: B2 - Application number: 16/719,474 - Filed: 2019-12-18 - Published: 2020-04-23 - Granted: 2020-09-08 - Earliest claimed: 2016-01-25 - Assignees: Alibaba Group Holding Limited - Applicants: Alibaba Group Holding Limited - Inventors: Tong Lin - Web page: https://patentgrep.com/patent/US10769795B2 ## CPC - G06F 18/253: {of extracted features} - G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} - G06F 18/23: Clustering techniques - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 7/174: involving the use of two or more images - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 2) 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. ### Claim 4 (depends on 2) 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. ### Claim 5 (depends on 4) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 1) 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. ### Claim 8 (depends on 1) 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. ### Claim 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. ### Claim 10 (depends on 9) 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. ### Claim 11 (depends on 10) 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. ### Claim 12 (depends on 10) 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. ### Claim 13 (depends on 12) 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. ### Claim 14 (depends on 9) 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. ### Claim 15 (depends on 9) 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. ### Claim 16 (depends on 9) 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. ### Claim 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-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more 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. ### Claim 18 (depends on 17) The computer-implemented system of claim 17, 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. ### Claim 19 (depends on 18) The computer-implemented system of claim 18, 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. ### Claim 20 (depends on 18) The computer-implemented system of claim 18, 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.