Image restoration method, image restoration device, electronic device and storage medium

By introducing structure reconstruction networks and color reconstruction networks of U-Net and transformer modules in image repair technology, the problem of difficult balance between structure reconstruction and color consistency in the existing technology is solved, and high-quality image repair is achieved, especially Outstanding performance in repairing complex shape defects.

CN118628406BActive Publication Date: 2025-02-11NORTHWEST UNIVERSITY FOR NATIONALITIES
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Patent Information

Application Number
CN202410460657.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-04-17
Publication Date
2025-02-11
Estimated Expiration
2044-04-17

AI Technical Summary

Technical Problem

Existing image repair techniques perform well when dealing with specific types of defects, but are difficult to achieve a good balance between structural reconstruction and color consistency, especially for defect repairs that are not effective in non-rectangular or irregular shapes.

Method used

A method of image repair is proposed, using image repair networks, including structural reconstruction networks and color reconstruction networks. The structure reconstruction network adopts the U-Net module and transformer module to restore the image structure through local and global feature extraction; the color reconstruction network ensures color consistency by color reconstruction of the structure reconstruction image.

Benefits of technology

A good balance between structural reconstruction and color consistency is achieved, and the accuracy and authenticity of image repair is improved, especially in repairing non-rectangular or irregular shape defects.

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Abstract

The present application provides an image restoration method, an image restoration device, an electronic device and a storage medium, which belong to the field of image processing technology. A target damaged image is obtained and input into an image restoration network. The image restoration network includes a structure reconstruction network and a color reconstruction network. The structure reconstruction network includes a U‑Net module and a transformer module. Local features of the target damaged image are extracted by the U‑Net module to obtain a reference feature map. Global features of the reference feature map are extracted by the transformer module to obtain a structure reconstructed image. Color reconstruction is performed on the structure reconstructed image by the color reconstruction network to obtain color features. The color features and the structure reconstructed image are fused to obtain a complete image of the target damaged image. The image restoration effect can be improved and the accuracy and authenticity of the restored image can be ensured.
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Citation Information

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