Image super-resolution reconstruction method and device based on anchor point guidance and boundary refinement cooperation

CN121961850BActive Publication Date: 2026-06-23HUNAN POLICE ACAD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN POLICE ACAD
Filing Date
2026-04-02
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing image super-resolution methods struggle to balance reconstruction quality and deployment efficiency within limited computational budgets. Lightweight models fall short in local detail recovery and global structural dependency characterization, failing to meet the dual requirements of high-quality reconstruction and lightweight deployment in practical applications.

Method used

An image super-resolution reconstruction method that combines anchor point guidance and boundary refinement is adopted. By leveraging the collaborative reconstruction units in the deep feature enhancement network, combined with global interaction of anchor point routing and boundary-aware region refinement processing, adaptive structural fusion and residual fusion are achieved, and features are optimized in stages to improve reconstruction quality.

Benefits of technology

It significantly improves the fidelity and efficiency of super-resolution reconstruction under limited computing budgets, and can be adapted to lightweight deployment scenarios such as intelligent monitoring and edge vision terminals, achieving collaborative optimization of global structural consistency and local detail fidelity.

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Abstract

The application relates to an image super-resolution reconstruction method and device based on anchor point guidance and boundary refinement cooperation, and belongs to the field of computer vision. The method comprises the following steps: performing shallow feature extraction on an input low-resolution image to obtain initial features; a deep feature enhancement network comprising multiple series cooperative reconstruction units is constructed, the initial features are input, and the initial features are sequentially enhanced by each unit; each unit performs anchor point routing global interaction and boundary perception region refinement on the input features to obtain global enhanced features and local refined features, the global enhanced features and the local refined features are fused through adaptive gating, and then the input features are fused through residual fusion, current cooperative enhanced features are output and are transmitted to the next unit, and finally, deep enhanced features are obtained; upsampling and pixel reconstruction are performed on the deep enhanced features, and a super-resolution image is output. The application considers long-distance structure dependence modeling and local boundary detail recovery, and realizes the cooperative optimization of global structure consistency and local detail fidelity under the constraint of lightweight deployment.
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