KR-20260066590-A - SERVER AND METHOD FOR SUPER-RESOLUTION AND SYSTEM INCLUDING THE SAME

KR20260066590AKR 20260066590 AKR20260066590 AKR 20260066590AKR-20260066590-A

Abstract

The present invention relates to a super-resolution server and method capable of varying the super-resolution method for image data (e.g., satellite imagery, aerial imagery, drone imagery, etc.) according to user requirements, and a system including the same. The super-resolution server may include a data collection module that receives image data and request information related to a user's needs regarding the image data, a super-resolution module that super-resolutions the image data according to the request information to generate a high-resolution image, and an output module that outputs the high-resolution image and reliability information regarding the high-resolution image.

Inventors

박현선
이용주
이여울
사공용협
김동철
구아롬
강명훈

Assignees

주식회사 메이사

Dates

Publication Date: 20260512
Application Date: 20250103

Claims (18)

External database for storing video data; A super-resolution server that receives the image data by communicating with the above external database; A user terminal that transmits request information related to the user's needs regarding the above image data to the super-resolution server; and A communication network that performs communication between the above external database and user terminal and the above super-resolution server, wherein The above super-resolution server is, A memory that stores at least one instruction, and It includes at least one processor that executes the above at least one instruction, and The above processor is, Receive the above video data and the above request information, Based on the above requirement information, the above image data is super-resolved to generate a high-resolution image, and Outputting the above high-resolution image and reliability information for the above high-resolution image Super-resolution system.
In Article 1, The above processor is, Receiving the image data from an external database that stores and manages the image data, and Receiving the request information from the user terminal of the above user Super-resolution system.
In Article 1, The above request information is, Coordinate information related to the coordinate area that the above user intends to analyze, and including purpose information related to the purpose that the above user intends to achieve through the above high-resolution image. Super-resolution system.
In Paragraph 3, The above purpose information is, Simple super-resolution meaning only of super-resolution for the above image data and including the user's selection regarding any one of the special analyses, which refers to specific data analysis through the above-mentioned video data. Super-resolution system.
In Paragraph 4, The above processor is, By dividing the objects included in the above image data, a division result for the image data is generated, and Based on the above-mentioned segmentation results and the above-mentioned requirement information, a super-resolution method is determined as a method for proceeding with the above-mentioned super-resolution, and A high-resolution image is generated by performing super-resolution on the image data according to the super-resolution method determined above. Super-resolution system.
In Article 5, The processor generates the segmentation result by segmenting the object using a pre-trained segmentation model based on a neural network. Super-resolution system.
In Article 6, The above segmentation model includes a Semantic Segmentation Model. Super-resolution system.
In Article 5, The above processor is, A first method based on up-scaling and One of the second methods based on a super-resolution model using a neural network is determined as the super-resolution method, and The above super-resolution model includes a Generative Artificial Intelligence model. Super-resolution system.
In Article 8, The above processor is, Determining the super-resolution method based on the objective information included in the above-mentioned requirement information Super-resolution system.
In Article 9, The above processor, when the above target information is the simple super-resolution, Determining the above second method as the above super-resolution method Super-resolution system.
In Article 9, The above processor, when the above target information is object analysis among the above special analyses, Determining the above second method as the above super-resolution method Super-resolution system.
In Article 9, The processor, when the target information is an area analysis or band analysis of a specific target among the special analyses, The result of combining the first method and the second method is determined by the super-resolution method. Super-resolution system.
In Article 12, The above processor is, For some of the multiple regions included in the above division result, the above first method is determined as the above super-resolution method, and For a part of the plurality of regions that is different from the part mentioned above, the second method is determined as the super-resolution method. Super-resolution system.
In Article 13, The above processor is, Based on the specific target mentioned above, determine the region of interest among the multiple regions included in the division result, and For the determined region of interest above, the first method is determined as the super-resolution method, and For regions other than the aforementioned region of interest, the above second method is determined as the above super-resolution method. Super-resolution system.
In Article 14, If the above specific object is a road and the above type of special analysis is an area analysis of the road, The above processor is, The area including the above road is determined as the region of interest, and For the region of interest including the above road, the above first method is determined as the above super-resolution method, and For the aforementioned other areas excluding the aforementioned road, the aforementioned second method is determined as the aforementioned super-resolution method. Super-resolution system.
In Article 14, If the above specific target is vegetation and the above type of special analysis is band analysis for said vegetation, The above processor is, The area containing the above vegetation is determined as the region of interest, and For the region of interest containing the above vegetation, the above first method is determined as the above super-resolution method, and For the above other areas excluding the above vegetation, the above second method is determined as the above super-resolution method. Super-resolution system.
In Article 1, The processor generates the reliability information based on the difference between the image data and the high-resolution image. Super-resolution system.
In Article 17, The processor compares the image data and the high-resolution image to generate the probability of agreement between an object included in the image data and an object included in the high-resolution image as the reliability information. Super-resolution system.

Description

Super-resolution server and method and system including the same The present invention relates to a super-resolution server, a method, and a system including the same. Specifically, the present invention relates to a super-resolution server and method capable of varying the super-resolution method for image data (e.g., satellite imagery, aerial imagery, drone imagery, etc.) according to user requirements, and a system including the same. The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art. Recently, image data, such as satellite imagery, aerial imagery, and drone footage, is being utilized to acquire information about buildings, roads, and nature. This image data is being used in various fields, including detecting the risk of wildfires and assessing vegetation indices. In this context, particularly since satellite imagery is captured from the ground up to high altitudes, there are cases where data analysis is difficult due to low resolution when utilizing such images. In such cases, super-resolution is performed to increase the resolution of the satellite imagery. However, generally, when performing super-resolution on satellite imagery, only known post-processing methods are executed in a batch manner, and there is no technological development regarding super-resolution methods tailored to specific user needs. Accordingly, there is a sufficient need for super-resolution related technologies that reflect user requirements. FIG. 1 illustrates a super-resolution system according to some embodiments of the present invention. FIG. 2 is a diagram illustrating the neural network structure of a neural network model used by a super-resolution server according to some embodiments of the present invention. FIG. 3 is a block diagram of a super-resolution server according to some embodiments of the present invention. Figure 4 illustrates image data according to some embodiments of the present invention. FIGS. 5 to 7 are drawings for explaining requirement information according to some embodiments of the present invention. FIG. 8 is a block diagram of a super-resolution module according to some embodiments of the present invention. FIG. 9 is a detailed block diagram of a super-resolution module according to some embodiments of the present invention. FIG. 10 is a conceptual diagram illustrating the operation of a super-resolution module according to some embodiments of the present invention. FIG. 11 is a detailed block diagram of an object segmentation unit included in a super-resolution module according to some embodiments of the present invention. FIGS. 12 and 13 are drawings for explaining the learning and execution steps of a segmentation model used by an object segmentation unit according to some embodiments of the present invention. FIG. 14 is a diagram illustrating a super-resolution method according to some embodiments of the present invention. FIG. 15 is a diagram illustrating the operation of a method determination unit in the case where the target information according to some embodiments of the present invention is simple super-resolution. FIG. 16 is a diagram illustrating the operation of a method determination unit in the case where the objective information according to some embodiments of the present invention is object analysis. FIG. 17 is a diagram illustrating the operation of a method determination unit in the case where the objective information according to some embodiments of the present invention is area analysis or band analysis. FIG. 18 is a conceptual diagram for explaining the operation of a method determination unit in the case where the objective information according to some embodiments of the present invention is an analysis of the area of a road. FIG. 19 is a conceptual diagram illustrating the operation of a method determination unit in the case where the objective information according to some embodiments of the present invention is a band analysis of vegetation. FIG. 20 is a block diagram of an output module according to some embodiments of the present invention. FIG. 21 is a drawing for explaining reliability information according to some embodiments of the present invention. FIG. 22 is a block diagram of a super-resolution server according to some other embodiments of the present invention. FIG. 23 is a flowchart of a super-resolution method according to some embodiments of the present invention. FIG. 24 is a detailed flowchart of step (S100) of FIG. 23 according to some embodiments of the present invention. FIG. 25 is a detailed flowchart of step (S200) of FIG. 23 according to some embodiments of the present invention. FIG. 26 is a detailed flowchart of step (S210) of FIG. 25 according to some embodiments of the present invention. FIG. 27 is a detailed flowchart of step (S220) of FIG. 25 according to some embodiments of the present invention. FIG. 28 is a detailed flowchart of step (S223) of