The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models

• URL: http://arxiv.org/abs/2204.02641v1
• Date: Wed, 6 Apr 2022 07:52:06 GMT
• Title: The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
• Authors: Julia Wolleb, Robin Sandk\"uhler, Florentin Bieder, Philippe C. Cattin
• Abstract summary: We build on a method for image-to-image translation using denoising diffusion implicit models. We apply our method to simulate the aging process on facial photos using a regression task. We also use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images.
• Score: 0.8999666725996974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, diffusion models were applied to a wide range of image analysis tasks. We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output. The main advantage of our approach is that the guidance during the denoising process is done by an external gradient. Consequently, the diffusion model does not need to be retrained for the different tasks on the same dataset. We apply our method to simulate the aging process on facial photos using a regression task, as well as on a brain magnetic resonance (MR) imaging dataset for the simulation of brain tumor growth. Furthermore, we use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images. We achieve convincing results for all problems.

Related papers

• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework. Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss. We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• Generalized Consistency Trajectory Models for Image Manipulation [59.576781858809355]
  Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. This work aims to unlock the full potential of consistency trajectory models (CTMs) by proposing generalized CTMs (GCTMs) We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.
  arXiv  Detail & Related papers  (2024-03-19T07:24:54Z)
• Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data [56.81246107125692]
  Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption. We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance. We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
  arXiv  Detail & Related papers  (2024-03-13T17:28:20Z)
• TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method [2.626378252978696]
  We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
  arXiv  Detail & Related papers  (2024-02-17T13:09:00Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning [0.0]
  We propose a method for denoising diffusion-weighted images (DWI) of the brain using a convolutional neural network trained on realistic, synthetic MR data. We show that the application of our method allows for a significant reduction in scan time by lowering the number of repeated scans.
  arXiv  Detail & Related papers  (2022-06-01T08:14:35Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.