Image region missing content generation method and system based on three-dimensional partial differential equation

By combining three-dimensional partial differential equations and deep learning, the progressive image generation network PDE-PINet solves the problem of poor structure and detail in the generation of missing content in image regions, and achieves more natural and detailed image generation results.

CN121544733BActive Publication Date: 2026-07-24BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
Filing Date
2025-10-29
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies suffer from problems such as easily confused image structure and loss of details in the generation of missing content in image regions, especially when generating complex textures and detailed images. Furthermore, deep learning methods lack rigorous mathematical foundations and utilization of frequency domain information.

Method used

We employ a progressive image region missing content generation network, PDE-PINet, based on three-dimensional partial differential equations. Combining partial differential equations with deep learning, we utilize a multi-stage generation strategy and a frequency domain attention mechanism to drive image generation using three-dimensional partial differential equations. By combining semantic segmentation maps and multi-scale feature extraction, we can achieve the generation of global structure and local details of images.

Benefits of technology

It maintains the overall structural coherence of the image in the generation of large missing regions, and the generated image has natural texture transitions and rich details, which improves the generation quality. It is suitable for complex texture and detailed images and has better generation effect and cross-dataset generalization ability.

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Abstract

The application provides a kind of image region missing content generation method and system based on three-dimensional partial differential equation, according to the method of the application, comprising: obtaining the image to be completed and its related auxiliary information;The complete image after completion is obtained by inputting the image to be completed and the auxiliary information into the pre-constructed image generation network model;Wherein, the image generation network model is the progressive image region missing content generation network PDE-PINet constructed based on three-dimensional partial differential equation.The application proposes an image region missing content generation method based on three-dimensional partial differential equation network, which combines partial differential equation with rigorous mathematical basis and deep learning to achieve better image region missing content generation effect;At the same time, through the mathematical constraint of three-dimensional partial differential equation, the structural distortion problem caused by the dependence of traditional deep learning method on data driving is effectively solved, and the coherence of the overall structure of the image can still be effectively maintained in large-area missing region generation.
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