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.

翻译：当前的自监督去噪技术取得了令人瞩目的成果，但其实际应用常受限于巨大的计算与内存需求，必须在推理速度与重建质量之间做出妥协。本文提出一种超轻量级模型以应对这一挑战，同时实现快速去噪与高质量图像恢复。该模型基于Noise2Noise训练框架构建——该框架消除了对干净参考图像或显式噪声建模的依赖——我们引入了一种创新的多阶段去噪流程，命名为Noise2Detail（N2D）。在推理过程中，该方法通过破坏噪声模式的空间相关性来生成中间平滑结构，随后对这些结构进行精细化处理，直接从含噪输入中恢复细微细节。大量测试表明，Noise2Detail在性能上超越了现有的无数据集技术，而仅需消耗极少计算资源。这种高效性、低计算成本与无数据方法的结合，使其成为生物医学成像领域的实用工具，既能克服因罕见复杂成像模态导致的洁净训练数据稀缺难题，又能实现满足实际应用需求的快速推理。