8 papers
Joint-decoupled iterative CBCT reconstruction with hybrid scatter estimation and voxel-adaptive beam hardening correction
Jianing Sun, Jean Michel Létang, Qixiang Sun +3
Cone-beam computed tomography (CBCT) is fundamentally challenged by scatter and beam hardening artifacts, which originate from X-ray scattering and the polychromatic nature of the…
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
Ligen Shi, Zengyu Pang, Chang Liu +2
Addressing the issues of severe noise and high frequency structural degradation in visible images under low-light conditions, this paper proposes a Near Infrared (NIR) aided low li…
Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations
Ligen Shi, Jun Qiu, Yuhang Zheng +2
Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, conventional Fourier feature mapp…
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
Ligen Shi, Ping Yang, Chang Liu +3
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically. Th…
Physics-Inspired Gaussian Kolmogorov-Arnold Networks for X-ray Scatter Correction in Cone-Beam CT
Xu Jiang, Huiying Pan, Ligen Shi +3
Cone-beam CT (CBCT) employs a flat-panel detector to achieve three-dimensional imaging with high spatial resolution. However, CBCT is susceptible to scatter during data acquisition…
Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
Ligen Shi, Xu Jiang, YunZe Liu +4
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…