alzayats/UDnet
Adaptive Uncertainty Distribution in Deep Learning for Unsupervised Underwater Image Enhancement
This project helps researchers and professionals working with underwater imagery to significantly improve the clarity and quality of their photos and video frames. It takes distorted, color-lost, and low-contrast underwater images as input and produces visually enhanced versions with adjusted contrast, saturation, and color. Marine biologists, oceanographers, archaeologists, and underwater photographers would find this particularly useful for analyzing and presenting their visual data.
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Use this if you need to automatically enhance a large collection of underwater images that suffer from common distortions and color issues, especially when you lack perfectly clear 'ground truth' examples for comparison.
Not ideal if your images are not from an underwater environment, or if you need to enhance images based on very specific, manually defined criteria rather than general visual improvement.
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59
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5
Language
Python
License
MIT
Category
Last pushed
Feb 20, 2025
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