5 citations · 5 across the 10 of their papers we have counts for
10 papers
Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features
Yao Guo, Ligen Shi, Shuchen Sun +2
Implicit neural representations provide a compact and continuous way to reconstruct dense light fields from sampled ray coordinates. However, fast light field reconstruction remain…
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…
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…
Infrared and Visible Image Fusion Based on Implicit Neural Representations
Shuchen Sun, Ligen Shi, Chang Liu +2
Infrared and visible light image fusion aims to combine the strengths of both modalities to generate images that are rich in information and fulfill visual or computational require…
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…
Iterative approach to reconstructing neural disparity fields from light-field data
Ligen Shi, Chang Liu, Xing Zhao +1
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 a…
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…
Matching entropy based disparity estimation from light field
Ligen Shi, Chang Liu, Di He +2
A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: textu…