Progressively Texture-Aware Diffusion for Contrast-Enhanced Sparse-View CT
Diffusion-based sparse-view CT (SVCT) imaging has achieved remarkable advancements in recent years, thanks to its more stable generative capability. However, recovering reliable image content and visually consistent textures is still a crucial challenge. In this paper, we present a Progressively Texture-aware Diffusion (PTD) model, a coarse-to-fine learning framework tailored for SVCT. Specifically, PTD comprises a basic reconstructive module PTD$_{\textit{rec}}$ and a conditional diffusion module PTD$_{\textit{diff}}$. PTD$_{\textit{rec}}$ first learns a deterministic mapping to recover the majority of the underlying low-frequency signals (i.e., coarse content with smoothed textures), wh...
| Authors | Tianqi Wang, Wenchao Du, Hongyu Yang |
|---|---|
| Primary category | cs.CV |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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