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Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data

Autor: Shi, Ligen, Jiang, Xu, Liu, YunZe, Liu, Chang, Yang, Ping, Guo, Shifeng, Zhao, Xing

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 art

Externí odkaz: http://arxiv.org/abs/2409.15731

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Iterative approach to reconstructing neural disparity fields from light-field data

Autor: Shi, Ligen, Liu, Chang, Zhao, Xing, Qiu, Jun

This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to address the inverse problem of NDF reconstruction from light-field data

Externí odkaz: http://arxiv.org/abs/2407.15380

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Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields

Autor: Shi, Ligen, Liu, Chang, Yang, Ping, Qiu, Jun, Zhao, Xing

In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically. This paper proposes a model that parameterizes the attenuation coefficie

Externí odkaz: http://arxiv.org/abs/2404.06991

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Matching entropy based disparity estimation from light field

Autor: Shi, Ligen, Liu, Chang, He, Di, Zhao, Xing, Qiu, Jun

A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able t

Externí odkaz: http://arxiv.org/abs/2210.15948

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