dlut-dimt/ReCoNet
ECCV 2022 | Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion.
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.
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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.
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50
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5
Language
Python
License
MIT
Category
Last pushed
Sep 30, 2022
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