No-New-Denoiser: A Critical Analysis of Diffusion Models for Medical Image Denoising

Laura Pfaff, Fabian Wagner, Nastassia Vysotskaya, Mareike Thies, Noah Maul, Siyuan Mei, Tobias Würfl, Andreas K. Maier

Abstract: Diffusion models, originally introduced for image generation, have recently gained attention as a promising image denoising approach. In this work, we perform comprehensive experiments to investigate the challenges posed by diffusion models when applied to medical image denoising. In medical imaging, retaining the original image content, and refraining from adding or removing potentially pathologic details is of utmost importance. Through empirical analysis and discussions, we highlight the trade-off between image perception and distortion in the context of diffusion-based denoising. In particular, we demonstrate that standard diffusion model sampling schemes yield a reduction in PSNR by up to 14% compared to one-step denoising. Additionally, we provide visual evidence indicating that diffusion models, in combination with stochastic sampling, have a tendency to generate synthetic structures during the denoising process, consequently compromising the clinical validity of the denoised images. Our thorough investigation raises questions about the suitability of diffusion models for medical image denoising, underscoring potential limitations that warrant careful consideration for future applications.