Jzy2017/TACL
TIP 2022 | Twin Adversarial Contrastive Learning for Underwater Image Enhancement and Beyond.
This project helps marine scientists, underwater photographers, or anyone working with underwater imagery to dramatically improve the clarity and quality of their photos and videos. You input blurry, low-contrast, or color-distorted underwater images, and it outputs enhanced versions with better visibility and color. This is ideal for researchers analyzing marine life or professionals documenting underwater environments.
No commits in the last 6 months.
Use this if you need to transform murky, discolored underwater images into clear, vibrant pictures for analysis, documentation, or public display.
Not ideal if you are looking to enhance images from land-based photography or other non-underwater environments.
Stars
65
Forks
6
Language
Python
License
—
Category
Last pushed
Jan 09, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Jzy2017/TACL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022