TL;DR
Experimental results demonstrate that IGG outperforms state-of-the-art image generation and editing models, producing realistic, high-quality images with preserved topology and fewer artifacts.
IGG (Image Generation informed by Geodesic dynamics), a novel framework that integrates topology-preserving geodesic principles into the diffusion-based generative process, where geometric object changes are learned as smooth and invertible smooth mappings from a given template/source image.
Experimental results demonstrate that IGG outperforms state-of-the-art image generation and editing models, producing realistic, high-quality images with preserved topology and fewer arti- facts.
Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps.
This work repurposes pretrained video generative models as a unified and data-efficient framework for geometry estimation, formulated innovatively as a next-frames prediction task, and inherits naturally structured knowledge and richer priors from the video model, enabling more data efficient and effective learning of...
Experiments show that TopoSegDiff produces more realistic and structurally faithful image-mask pairs, reduces topology inconsistency, and consistently improves downstream segmentation performance when used for synthetic augmentation, which support topology-preserving conditioning as an effective route toward more relia...
GAN-based image translation has been widely used for cross-domain medical image synthesis. However, most existing methods follow a one-to-one mapping paradigm, requiring a separate model for each target domain and increasing the training cost in multi-target tasks such as multi-sequence MRI synthesis. Although recent o...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
