Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling technique, which generates clean images by averaging diffusion sampling outputs initialized with a pair of symmetric noises. Finally, we train a supervised denoising network using noisy images paired with the denoised outputs generated by the diffusion model. Our results demonstrate that DiffDenoise outperforms existing state-of-the-art methods in both synthetic and real-world medical image denoising tasks. We provide both a theoretical foundation and practical insights, demonstrating the method's effectiveness across various medical imaging modalities and anatomical structures.

翻译：近年来，已有许多自监督去噪方法被提出。然而，这些方法往往过度平滑图像，导致医学应用中至关重要的细微结构丢失。本文提出DiffDenoise，一种专为医学图像设计的强大自监督去噪方法，旨在保留高频细节。我们的方法包含三个阶段。首先，我们在噪声图像上训练一个扩散模型，使用预训练的盲点网络输出作为条件输入。接着，我们引入一种新颖的稳定反向采样技术，该技术通过对以一对对称噪声初始化生成的扩散采样输出进行平均来生成干净图像。最后，我们使用噪声图像与扩散模型生成的去噪输出配对，训练一个监督去噪网络。我们的结果表明，DiffDenoise在合成和真实世界的医学图像去噪任务中均优于现有的最先进方法。我们提供了理论基础和实践见解，证明了该方法在各种医学成像模态和解剖结构上的有效性。