Guidance-base Diffusion Models for Improving Photoacoustic Image Quality
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Photoacoustic(PA) imaging is a non-destructive and non-invasive technology for visualizing minute blood vessel structures in the body using ultrasonic sensors. In PA imaging, the image quality of a single-shot image is poor, and it is necessary to improve the image quality by averaging many single-shot images. Therefore, imaging the entire subject requires high imaging costs. In our study, we propose a method to improve the quality of PA images using diffusion models. In our method, we improve the reverse diffusion process using sensor information of PA imaging and introduce a guidance method using imaging condition information to generate high-quality images.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 36 references that are in the map. First posted on arXiv 10 Feb 2025.
Builds on
29 of 36 references.
Closest ancestors · 3
Same direction, newest first
- 2023Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models29 Jul
- 2023Ambiguous Medical Image Segmentation using Diffusion Models10 Apr
- 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization4 Apr
Other references · 26
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
- 2023Consistency Modelscited by 1,778
14 more
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
- 2021Image Super-Resolution via Iterative Refinementcited by 1,506
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
- 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
- 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
- 2023Improved Techniques for Training Consistency Modelscited by 430
- 2023Universal Guidance for Diffusion Modelscited by 426
- 2022Diffusion Models already have a Semantic Latent Spacecited by 324
- 2023Training-Free Layout Control with Cross-Attention Guidancecited by 312
- 2022Perception Prioritized Training of Diffusion Modelscited by 301
- 2022Diffusion models as plug-and-play priorscited by 230
- 2023Image Restoration with Mean-Reverting Stochastic Differential Equationscited by 182
- 2022Self-Guided Diffusion Modelscited by 36
- 2024NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Mergingcited by 25
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- 2024Using Spatial Diffusions for Optoacoustic Tomography Image Reconstruction
- 2023Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty
- 2024Ultrasound Imaging based on the Variance of a Diffusion Restoration Model
- 2023Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles
- 2024Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models