Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging

arXiv:2508.09655 · cs.CV · Submitted 2025-08-13 · Read on arXiv

cs.CV

Submitted: 2025-08-13

Updated: 2026-09-26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• NON-LINE-OF-SIGHT IMAGING — 14× in this paper
• Ablation — 9× in this paper · explained in 1 episode
• Accuracy — 9× in this paper · explained in 1 episode
• Loss Function — 9× in this paper · explained in 2 episodes
• Regularization — 9× in this paper · explained in 3 episodes
• Inverse Problem — 8× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• Noise-Adapted Neural Operator — 6× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Noise Level Estimation — 5× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Latent Representation — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• DeepONet — 1× in this paper · explained in 2 episodes
• Dynamic Adaptation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Hierarchical Framework — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Mathematical Sciences of Beijing Normal University — 1× in this paper
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Peak Signalto-Noise Ratio — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Total Variation — 1× in this paper
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
• Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
• ShapeNet: An Information-Rich 3D Model Repository

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