Nonlinear Noise2Noise for HDR Image Denoising
Research Paper#Image Denoising, Machine Learning, HDR Imaging🔬 Research|Analyzed: Jan 3, 2026 08:41•
Published: Dec 31, 2025 11:30
•1 min read
•ArXivAnalysis
This paper addresses a key limitation of the Noise2Noise method, which is the bias introduced by nonlinear functions applied to noisy targets. It proposes a theoretical framework and identifies a class of nonlinear functions that can be used with minimal bias, enabling more flexible preprocessing. The application to HDR image denoising, a challenging area for Noise2Noise, demonstrates the practical impact of the method by achieving results comparable to those trained with clean data, but using only noisy data.
Key Takeaways
- •Addresses the bias problem in Noise2Noise caused by nonlinearities.
- •Provides a theoretical framework for analyzing the effects of nonlinear functions.
- •Identifies a class of nonlinear functions with minimal bias.
- •Applies the method to HDR image denoising, a challenging application.
- •Achieves results comparable to those trained with clean data, but using only noisy data.
Reference / Citation
View Original"The paper demonstrates that certain combinations of loss functions and tone mapping functions can reduce the effect of outliers while introducing minimal bias."