DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings
DynGhost is a transformer architecture designed for dynamic ghost imaging that reconstructs spatial information from single-pixel detectors. It uses spatial-temporal attention and temporal consistency losses to exploit motion coherence across frames, overcoming limitations of static deep learning models. This approach achieves superior reconstruction quality and operates faster than iterative solvers.