bibtext: @InProceedings{ Pfa_NoNewDenoiser_MICCAI2024,
                   author = { Pfaff, Laura and Wagner, Fabian and Vysotskaya, Nastassia and Thies, Mareike and Maul, Noah and Mei, Siyuan and Wuerfl, Tobias and Maier, Andreas },
                   title = { { No-New-Denoiser: A Critical Analysis of Diffusion Models for Medical Image Denoising } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
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.
title: No-New-Denoiser: A Critical Analysis of Diffusion Models for Medical Image Denoising
authors: Pfaff, Laura Wagner, Fabian Vysotskaya, Nastassia Thies, Mareike Maul, Noah Mei, Siyuan Wuerfl, Tobias Maier, Andreas