MID: A Self-supervised Multimodal Iterative Denoising Framework

• URL: http://arxiv.org/abs/2511.00997v1
• Date: Sun, 02 Nov 2025 16:13:52 GMT
• Title: MID: A Self-supervised Multimodal Iterative Denoising Framework
• Authors: Chang Nie, Tianchen Deng, Zhe Liu, Hesheng Wang,
• Abstract summary: Real-world data is frequently corrupted by complex, non-linear noise.<n>We propose a novel self-supervised multimodal iterative denoising framework, MID.<n>Experiments across four classic computer vision tasks demonstrate MID's robustness, adaptability, and consistent state-of-the-art performance.
• Score: 21.9870371385388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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