sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows

2026-08-29

AI Summary

本文通过引入一种新的基于归一化流的框架来估计相机设置，以改进噪声建模并生成多样化的噪声图像，从而解决真实世界图像去噪问题。

Abstract

Noise poses a widespread challenge in signal processing, particularly when it comes to denoising images. Although convolutional neural networks (CNNs) have exhibited remarkable success in this field, they are predicated upon the belief that noise follows established distributions, which restricts their practicality when dealing with real-world noise. To overcome this limitation, several efforts have been taken to collect noisy image datasets from the real world. Generative methods, employing techniques such as generative adversarial networks (GANs) and normalizing flows (NFs), have emerged as a solution for generating realistic noisy images. Recent works model noise using camera metadata, however requiring metadata even for sampling phase. In contrast, in this work, we aim to estimate the underlying camera settings, enabling us to improve noise modeling and generate diverse noise distributions. To this end, we introduce a new NF framework that allows us to both classify noise based on camera settings and generate various noisy images. Through experimental results, our model demonstrates exceptional noise quality and leads in denoising performance on benchmark datasets.

Problem

• Noise
• Signal Processing
• Denoising Images
• Convolutional Neural Networks (CNNs)
• Real-World Noise

Innovation

• Normalizing Flows
• Noise Modeling
• Camera Settings Estimation
• Denoising Performance