In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI pre-trains a model with masking and fine-tunes it for denoising of a single image with unseen noise degradation. Concretely, the proposed MPI comprises two key procedures: 1) Masked Pre-training involves training a model on multiple natural images with random masks to gather generalizable representations, allowing for practical applications in varying noise degradation and even in distinct image types. 2) Iterative filling is devised to efficiently fuse pre-trained knowledge for denoising. Similar to but distinct from pre-training, random masking is retained to bridge the gap, but only the predicted parts covered by masks are assembled for efficiency, which enables high-quality denoising within a limited number of iterations. Comprehensive experiments across various noisy scenarios underscore the notable advances of proposed MPI over previous approaches with a marked reduction in inference time. Code is available at https://github.com/krennic999/MPI.git.

翻译：本文发现，采用掩码策略在大量通用图像上训练的模型，其内部已自然蕴含自然图像的分布知识，从而自发具备强大的图像去噪潜力。基于此发现，我们提出一种新型零样本去噪范式——掩码预训练与迭代填充（MPI）。MPI通过掩码策略预训练模型，并针对存在未知噪声退化的单幅图像进行微调以实现去噪。具体而言，所提MPI包含两个关键步骤：1）掩码预训练：在多个自然图像上通过随机掩码训练模型以获取泛化表征，从而适应不同噪声退化场景甚至异构图像类型的实际应用；2）迭代填充：高效融合预训练知识实现去噪。与预训练相似但不同的是，我们保留了随机掩码以弥合差距，但仅组装掩码覆盖的预测部分以提升效率，从而在有限迭代次数内实现高质量去噪。在多种噪声场景下的综合实验表明，所提MPI相较于既有方法具有显著优势，且推理时间大幅缩短。代码发布于https://github.com/krennic999/MPI.git。