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Zero-Learning Fast Medical Image Fusion

Zero-Learning Fast Medical Image Fusion

High-quality image fusion

Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pre-trained neural networks to generate a single image containing features from multi-modal sources. Our method can be applied to any number of input sources.

Deep Neural NetworksImages
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C4DT
Inactive
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Unknown
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Image and Visual Representation Lab

Image and Visual Representation Lab
Sabine Süsstrunk

Prof. Sabine Süsstrunk

The Image and Visual Representation Lab (IVRL) performs research that is primarily focused on the capture, analysis, and reproduction of color images. Aiming to improve everyone’s photographic experience, we develop algorithms and systems that help us understand, process, and measure images.
Their research areas are computational photography, color image processing, computer vision, and image quality.

This page was last edited on 2024-04-14.