DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
It addresses the need for preserving high-frequency details in medical images, which is crucial for accurate diagnosis, though it appears incremental as it builds on existing diffusion and self-supervised techniques.
The paper tackled the problem of self-supervised medical image denoising, which often loses fine structures, by proposing DiffDenoise, a method that outperformed state-of-the-art approaches in synthetic and real-world tasks.
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.