Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916816172875776 |
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| author | Shi, Ligen Jiang, Xu Liu, YunZe Liu, Chang Yang, Ping Guo, Shifeng Zhao, Xing |
| author_facet | Shi, Ligen Jiang, Xu Liu, YunZe Liu, Chang Yang, Ping Guo, Shifeng Zhao, Xing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15731 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data Shi, Ligen Jiang, Xu Liu, YunZe Liu, Chang Yang, Ping Guo, Shifeng Zhao, Xing Image and Video Processing 68U05, 65D18 I.4.5; I.4.10 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. |
| title | Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data |
| topic | Image and Video Processing 68U05, 65D18 I.4.5; I.4.10 |
| url | https://arxiv.org/abs/2409.15731 |