Noise Controlled CT Super-Resolution with Conditional Diffusion Model
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.
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 2 of its 11 references that are in the map. First posted on arXiv 13 Feb 2025.
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2 of 11 references.
References · 2
- 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
- 2022Scalable Adaptive Computation for Iterative Generationcited by 190
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- 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
- 2026CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
- 2024Arbitrary-steps Image Super-resolution via Diffusion Inversion