Histogram-free single-photon avalanche diode array imaging in dense scattering media via spatiotemporal multi-head long short-term memory networks

Single-photon imaging presents a promising solution for imaging in dense scattering media. Yet, existing methods predominantly rely on per-pixel photon count histograms for preprocessing, which treat pixels separately and neglect intrinsic spatio-temporal correlations. This is an inherent limitation that is drastically amplified by scattering effects, which diffusively spread signal information across adjacent pixels simultaneously. To capture these global correlations, we propose a histogram-free SPAD array sensing method that directly exploits the raw photon arrival sequences. Specifically, we design a multi-head LSTM sequence model with a dual-task training objective to jointly optimize classification and depth regression. Our approach is validated by extensive experiments on a self-constructed dataset, acquired via a real-world hardware prototype under varying fog densities. Our histogram-free approach outperforms complex baselines in scattering environments by preserving temporal information, while its lightweight design enables efficient deployment in resource-constrained systems.