无需干净数据，轻量级去噪模型实现快速高保真医学图像修复。
Lightweight Data-Free Denoising for Detail-Preserving Biomedical Image Restoration
- 基于Noise2Noise框架，分阶段去噪并破坏噪声空间相关性以保留细节
- 在不依赖清洁图像情况下，速度远超现有方法且计算成本极低
- 适合缺乏清洁数据的医学影像场景，尤其适用于实时诊断应用
当前自监督去噪技术虽表现优异，但因计算与内存开销大，实际应用受限，常需在推理速度与重建质量间权衡。本文提出一种超轻量模型，解决该问题，实现快速且高质量的图像修复。基于无需干净参考图像或显式噪声建模的Noise2Noise训练框架，我们设计了名为Noise2Detail（N2D）的多阶段去噪流程。推理时，该方法通过破坏噪声的空间相关性生成中间平滑结构，并直接从噪声输入中重构精细细节。大量测试表明，N2D在性能上超越现有无数据集技术，同时仅需极小计算资源。其高效、低成本及无数据依赖特性使其成为医学成像中的有力工具，克服罕见复杂成像模式下清洁数据稀缺的问题，支持实际应用中的快速推理。
原文摘要 · Abstract (English)
Current self-supervised denoising techniques achieve impressive results, yet their real-world application is frequently constrained by substantial computational and memory demands, necessitating a compromise between inference speed and reconstruction quality. In this paper, we present an ultra-lightweight model that addresses this challenge, achieving both fast denoising and high quality image restoration. Built upon the Noise2Noise training framework-which removes the reliance on clean reference images or explicit noise modeling-we introduce an innovative multistage denoising pipeline named Noise2Detail (N2D). During inference, this approach disrupts the spatial correlations of noise patterns to produce intermediate smooth structures, which are subsequently refined to recapture fine details directly from the noisy input. Extensive testing reveals that Noise2Detail surpasses existing dataset-free techniques in performance, while requiring only a fraction of the computational resources. This combination of efficiency, low computational cost, and data-free approach make it a valuable tool for biomedical imaging, overcoming the challenges of scarce clean training data-due to rare and complex imaging modalities-while enabling fast inference for practical use.
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