# Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data

arXiv: https://arxiv.org/abs/2409.15731 · HTML: https://mlsanity.com/paper/2409.15731/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT](https://mlsanity.com/paper/2412.05853/) (2024)
- [Low performing pixel correction in computed tomography with unrolled network and synthetic data training](https://mlsanity.com/paper/2601.20995/) (2026)
- [SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Training](https://mlsanity.com/paper/2602.11880/) (2026)
- [TRACE: Two-Stage Detector-Response Estimation With Angular Cosine Expansion for Ring Artifact Correction in Photon-Counting CT](https://mlsanity.com/paper/2609.15834/) (2026)

## Abstract (arXiv)

Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe artifacts in the sinogram data. The proposed method leverages implicit neural representation (INR) to correct defective pixel response values using implicit continuous functions and simultaneously learns stripe features in the angular direction of the sinogram data. These two components are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in removing ring artifacts while maintaining the clarity of CT images.