chi-kaichen/Trinity-Net
K. Chi, Y. Yuan, and Q. Wang*, “Trinity-Net: Gradient-Guided Swin Transformer-Based Remote Sensing Image Dehazing and Beyond,” IEEE Transactions on Geoscience and Remote Sensing (T-GRS), 2023.
This project helps improve the clarity and detail of remote sensing images by removing haze, fog, and mist. It takes hazy satellite or aerial photographs as input and outputs clear, dehazed versions, making features easier to identify. Remote sensing analysts, cartographers, environmental scientists, and urban planners would use this to get better insights from their imagery.
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Use this if you need to process remote sensing images that are obscured by atmospheric conditions to reveal clearer ground details.
Not ideal if you are working with non-image data or require real-time processing for dynamic scenes.
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Jan 31, 2023
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