无需配对数据，用迭代去噪法自动清除复杂噪声。
MID: A Self-supervised Multimodal Iterative Denoising Framework
- 通过迭代加噪学习噪声估计与去除网络。
- 在4个视觉任务中达到最先进性能。
- 适用于生物医学等非图像领域，无需干净数据标注。
数据去噪在科学与工程领域长期面临挑战。真实世界数据常受复杂非线性噪声污染，传统基于规则的去噪方法难以应对。为此，我们提出一种新型自监督多模态迭代去噪（MID）框架。MID将采集到的噪声数据建模为非线性噪声累积过程中的一个状态，通过迭代引入更多噪声，学习两个神经网络：一个用于估计当前噪声步数，另一个用于预测并减去相应的噪声增量。针对复杂非线性污染，MID采用一阶泰勒展开对噪声过程局部线性化，实现有效迭代去除。关键优势在于无需成对的清洁-噪声数据集，可直接从噪声输入中学习噪声特性。在四个经典计算机视觉任务上的实验表明，MID具备强鲁棒性、高适应性，并持续取得最先进性能；此外，在生物医学与生物信息学任务中也展现出优异表现。
原文摘要 · Abstract (English)
Data denoising is a persistent challenge across scientific and engineering domains. Real-world data is frequently corrupted by complex, non-linear noise, rendering traditional rule-based denoising methods inadequate. To overcome these obstacles, we propose a novel self-supervised multimodal iterative denoising (MID) framework. MID models the collected noisy data as a state within a continuous process of non-linear noise accumulation. By iteratively introducing further noise, MID learns two neural networks: one to estimate the current noise step and another to predict and subtract the corresponding noise increment. For complex non-linear contamination, MID employs a first-order Taylor expansion to locally linearize the noise process, enabling effective iterative removal. Crucially, MID does not require paired clean-noisy datasets, as it learns noise characteristics directly from the noisy inputs. Experiments across four classic computer vision tasks demonstrate MID's robustness, adaptability, and consistent state-of-the-art performance. Moreover, MID exhibits strong performance and adaptability in tasks within the biomedical and bioinformatics domains.
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