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arXiv Computer Vision By Lianfang Wang, Xueying Liu, Junnan Dong, Qinghua Zhang, Jun Liu, Yong Wang, Yuping Duan

Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight Imaging

Read the original on arXiv Computer Vision →

The paper introduces the Measurement-Adaptive Light-Cone Transform (MALCT), a physics-guided framework that maintains the efficient Fourier-domain inversion of traditional light-cone transform methods while adapting to real-world measurement noise and deviations in non-line-of-sight imaging. MALCT integrates an analytical response with a learned response via measurement-conditioned weighting and predicts a frequency-dependent regularization map within a unified variational formulation. An attention-enhanced spatio-temporal feature extraction module captures long-range temporal dependencies and local spatial correlations, enabling robust reconstruction under low-signal conditions and with reduced acquisition time, as demonstrated on synthetic data and real measurements from various NLOS systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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