caiyuanhao1998/PNGAN
"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training" (NeurIPS 2021)
This project helps image processing researchers and developers create highly realistic noisy versions of clean images. By taking a clean image, it generates a synthetic noisy image that closely mimics real-world sensor noise. The end user is typically a researcher or engineer working on developing and testing image denoising algorithms.
146 stars. No commits in the last 6 months.
Use this if you need to generate high-fidelity, synthetic noisy images to train and evaluate image denoising models, especially when real noisy-clean image datasets are scarce or expensive to acquire.
Not ideal if you primarily need to remove noise from existing noisy images, as this tool focuses on generating realistic noise rather than directly performing denoising.
Stars
146
Forks
19
Language
Python
License
MIT
Category
Last pushed
Jun 13, 2025
Commits (30d)
0
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