[ICCV 2023] Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising && [Arxiv 2023] Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
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Updated
Mar 24, 2024 - Python
[ICCV 2023] Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising && [Arxiv 2023] Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
[TPAMI 2023 / ACMMM 2022 Best Paper Runner-Up Award] Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling (a Data Perspective)
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising
sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows, in ICLR 2024
A flexible Python framework for generating, fitting, and visualizing noisy nonlinear data. Perfect for educational purposes, algorithm testing, and demonstrating statistical concepts. Includes tools for various noise models, custom function fitting, robust error metrics, and publication-quality visualizations
Simulate and estimate the trajectories of two balls using particle filters. Includes noisy observations, particle filtering, error calculations, and visualizations. Requires Python, `numpy`, and `matplotlib`.
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