无需知道噪声分布，就能高效去噪的新方法
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
- 用可学习的单调网络自动模拟去噪过程
- 在多种复杂噪声下性能超越现有方法
- 适合实际场景中噪声未知的情况
自监督图像去噪方法通常依赖于架构约束或需预先知晓噪声分布的损失函数，以避免平凡的恒等映射。传统方法如Noisier2Noise或Recorrupted2Recorrupted通过向噪声图像添加合成噪声生成训练对，虽有效但要求精确掌握噪声分布，而该条件常无法满足。本文提出学习去重污染（Learning to Recorrupt, L2R），一种无需噪声分布知识的去噪技术。该方法引入一个可学习的单调神经网络，通过最小-最大鞍点目标学习去重污染过程。所提方法在非典型及重尾噪声分布（如对数伽马、拉普拉斯、空间相关噪声）以及信号依赖型噪声模型（如泊松-高斯噪声）下均达到当前最优性能。
原文摘要 · Abstract (English)
Self-supervised image denoising methods have traditionally relied on either architectural constraints or specialized loss functions that require prior knowledge of the noise distribution to avoid the trivial identity mapping. Among these, approaches such as Noisier2Noise or Recorrupted2Recorrupted, create training pairs by adding synthetic noise to the noisy images. While effective, these recorruption-based approaches require precise knowledge of the noise distribution, which is often not available. We present Learning to Recorrupt (L2R), a noise distribution-agnostic denoising technique that eliminates the need for knowledge of the noise distribution. Our method introduces a learnable monotonic neural network that learns the recorruption process through a min-max saddle-point objective. The proposed method achieves state-of-the-art performance across unconventional and heavy-tailed noise distributions, such as log-gamma, Laplace, and spatially correlated noise, as well as signal-dependent noise models such as Poisson-Gaussian noise.
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