dlut-dimt/ReCoNet

ECCV 2022 | Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion.

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Emerging

This project helps operations engineers, surveillance analysts, or medical imaging specialists combine information from different types of cameras or sensors, like thermal and standard visible light. It takes two aligned images of the same scene, each captured by a different sensor, and merges them into a single, clearer image that contains details from both. This allows for a more comprehensive understanding of the scene than either individual image could provide.

No commits in the last 6 months.

Use this if you need to combine visual information from different imaging modalities, such as infrared and visible light, into a single, enhanced image for better analysis or decision-making.

Not ideal if your input images are significantly misaligned, as the registration module may struggle with large deformations between different sensor types.

multi-modal-imaging thermal-imaging surveillance remote-sensing medical-imaging
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 10 / 25

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Stars

50

Forks

5

Language

Python

License

MIT

Last pushed

Sep 30, 2022

Commits (30d)

0

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