DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction
From Daily Paper Cast by Jingwen Liang, Gengyu Wang
April 28, 2026 · 25 min · Episode 1805
About this episode
The episode discusses DiffNR, a framework for improving 3D tomographic reconstruction using diffusion-enhanced neural representation optimization.
🤗 Upvotes: 27 | eess.IV, cs.CV Authors: Shiyan Su, Ruyi Zha, Danli Shi, Hongdong Li, Xuelian Cheng Title: DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction Arxiv: http://arxiv.org/abs/2604.21518v1 Abstract: Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model designed to correct artifacts in degraded slices. We integrate specialized conditioning layers into the network and develop tailored data curation strategies to support model finetuning. During reconstruction, SliceFixer periodically generates pseudo-reference volumes, providing auxiliary 3D perceptual supervision to fix underconstrained regions. Compared to prior methods that embed CT solvers into time-consuming iterative denoising, our repair-and-augment strategy avoids frequent diffusion model queries, leading to better runtime performance. Extensive experiments…
People in this episode
Hosts: Jingwen Liang, Gengyu Wang
Topics covered
- 3D Tomographic Reconstruction
- Neural Representations
- Diffusion Models
- Image Processing
- Computer Vision
Keywords
- DiffNR
- Neural Representation
- Sparse-View
- Tomographic Reconstruction
- Diffusion Priors
- SliceFixer
- PSNR
- Image Artifacts
Mentioned in this episode
Organizations: arxiv
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