An infrared optical image fusion method and system based on manifold adaptive filtering

By employing manifold adaptive multi-scale decomposition and structure perception, the problems of structural distortion, detail ghosting, and insufficient contrast in infrared and visible light image fusion were solved, achieving high-quality multimodal information fusion in complex scenes and improving the contrast and visual effect of the fused image.

CN122265055BActive Publication Date: 2026-07-24WUHAN UNIV
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Patent Information

Application Number
CN202610729535.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-26
Publication Date
2026-07-24
Estimated Expiration
2046-05-26

AI Technical Summary

Technical Problem

Existing infrared and visible light image fusion methods have shortcomings in terms of structure preservation, detail consistency, and fusion contrast. In particular, in scenarios with significant differences in multimodal structures or complex contrasts between thermal targets and backgrounds, structural distortion, detail ghosting, and insufficient contrast in the fusion results are prone to occur.

Method used

We employ a manifold adaptive multi-scale decomposition and structure-aware approach. By constructing a manifold adaptive scale space for multi-scale decomposition, we utilize the structural features of the Riemannian manifold to build a saliency measure and design a structure-aware gradient transparency vector field fusion mechanism to achieve high-quality fusion of multimodal information.

Benefits of technology

It significantly improves the contrast of the fused image, maintains structural consistency, effectively suppresses ghosting of details, and maintains good structural integrity and visual naturalness in complex environments.

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Abstract

The application discloses an infrared optical image fusion method and system based on manifold adaptive decomposition, and belongs to the technical field of image processing and multi-modal information fusion, mainly including the steps of image structure scale feature estimation, adaptive multi-scale decomposition construction, structure perception fusion mechanism design and gradient domain reconstruction. Experimental results show that the method has significant advantages in maintaining structure integrity, enhancing details and textures, and improving fusion contrast, and can obtain high-quality fusion results with clear structure and natural vision in a complex environment.
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