A Physics-Informed Synthetic Generation Framework and Dataset for TIE Phase Map Denoising in High-Speed Energetic Flows - Dataset

High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. Phase maps reconstructed via the Transport of Intensity Equation (TIE) suffer from spatially correlated low-frequency artifacts introduced by the inverse Laplacian solver, which obscure critical flow structures such as jet plumes, shockwave fronts, and density gradients. Supervised deep learning methods for artifact suppression require paired clean-noisy training data — a resource that is fundamentally unavailable in real high-speed experiments, where every frame represents a physically unique, non-repeatable flow state. We address this by developing a physics-informed synthetic dataset generation framework where clean targets are procedurally generated using physically plausible gas flow morphologies — including compressible jet plumes, turbulent eddy fields, density fronts, periodic air pockets, and expansion fans — and passed through a forward TIE simulation followed by inverse Laplacian reconstruction to produce realistic noisy phase maps. We publicly release a curated subset of 25,000 paired clean–noisy gas flow phase map images at 256×256 resolution, constructed using this framework. This dataset is intended to support reproducible research in physics-informed deep learning for optical flow diagnostics, TIE phase retrieval, and related computational imaging tasks.