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Diffusion models advance constrained optimization and CT denoising

By PulseAugur Editorial · [2 sources] ·

Two research papers explore advancements in diffusion models for image denoising tasks. The first paper, "Denoising as Projection," proposes a method to use a pre-trained denoiser as an approximate projection onto learned data geometry for constrained optimization. The second paper, "FoundDiff," introduces a foundational diffusion model for generalizable low-dose computed tomography (CT) denoising, employing a two-stage strategy for dose and anatomy perception and adaptive denoising. AI

IMPACT These papers advance diffusion model capabilities for constrained optimization and specialized image denoising tasks, potentially improving applications in scientific research and medical imaging.

RANK_REASON Two academic papers published on arXiv detailing new methods for diffusion models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Diffusion models advance constrained optimization and CT denoising

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Two academic papers published on arXiv detailing new methods for diffusion models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Runyu Zhang, Jiawei Zhang, Gioele Zardini, Saurabh Amin, Asuman Ozdaglar ·

    Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion

    arXiv:2608.29507v1 Announce Type: cross Abstract: Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-specific objectives. A common approach is to guide the reverse diffusion process usin…

  2. arXiv cs.CV TIER_1 English(EN) · Zhihao Chen, Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, Jun Zhao, Hongming Shan ·

    FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising

    arXiv:2508.17299v2 Announce Type: replace Abstract: Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existin…

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