7 papers
SAOT: An Enhanced Locality-Aware Spectral Transformer for Solving PDEs
Chenhong Zhou, Jie Chen, Zaifeng Yang
Neural operators have shown great potential in solving a family of Partial Differential Equations (PDEs) by modeling the mappings between input and output functions. Fourier Neural…
CoDe-NeRF: Neural Rendering via Dynamic Coefficient Decomposition
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Neural Radiance Fields (NeRF) have shown impressive performance in novel view synthesis, but challenges remain in rendering scenes with complex specular reflections and highlights.…
UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like N…
Dual-Balancing for Physics-Informed Neural Networks
Chenhong Zhou, Jie Chen, Zaifeng Yang +1
Physics-informed neural networks (PINNs) have emerged as a new learning paradigm for solving partial differential equations (PDEs) by enforcing the constraints of physical equation…
Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement
Xingxing Yang, Jie Chen, Zaifeng Yang
Image decomposition offers deep insights into the imaging factors of visual data and significantly enhances various advanced computer vision tasks. In this work, we introduce a nov…
Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation
Xingxing Yang, Jie Chen, Zaifeng Yang
NIR-to-RGB spectral domain translation is a challenging task due to the mapping ambiguities, and existing methods show limited learning capacities. To address these challenges, we…
Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues
Xingxing Yang, Jie Chen, Zaifeng Yang
Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate…