We introduce Simulation-Based Imaging (SBI), a framework for non-destructive acoustic imaging in which machine learning models trained entirely on simulated data serve as real-time solvers for the acoustic inverse problem. A high-fidelity nodal Discontinuous Galerkin forward solver generates large training datasets by randomizing inclusion geometry within a unit-cube domain; a 2D convolutional neural network then learns a direct mapping from boundary pressure measurements to a 32 by 32 by 32 voxel reconstruction of the interior. The trained model reliably recovers inclusion position and size from 144 boundary sensors with no prior knowledge of inclusion count or geometry. Reconstruction error degrades by only 13% under 5% additive measurement noise, and just 17% of the sensor array (24 of 144 sensors) suffices for quality within 4% of full coverage. These results establish SBI as a viable proof-of-concept imaging device whose complexity resides in software rather than hardware, opening a path toward cheap, portable, deployable imaging systems.
This work observes that in both incoherent and coherent optical imaging, the irradiance patterns corresponding to two phase diverse measurements associated with the same test object have implicit local correlations, which may be learned by a suitable deep network.
J. Birdi, Tamal Majumder, Deb Proshad Halder et al.· Journal of Physics: Photonic...· 0 citations
The Sensor Attention Network is proposed, a Transformer-based architecture that treats the full time series of each sensor as a token and maps raw measurements directly to the reconstructed image without invoking the system matrix at inference, supporting real-time PAT reconstruction.
Mary Anjaley Josy John, Shibili Said, Imad Barhumi et al.· arXiv.org· 0 citations
The central finding is that domain-specific encoder design, rather than physics-informed loss terms, drives reconstruction accuracy in an amortised neural framework for three-dimensional acoustic diffraction tomography.
Ju O. Kim, Deokwoo Lee· Measurement science and tech...· 0 citations
Imaging through scattering media continues to be a persistent challenge in optical imaging due to the fact that scattering disrupts the direct relationship between the object and the measured signal. Once this relationship is degraded, reconstructing the original scene turns into a challenging inverse problem, for whic...
R. A. Saleh, Salah F. S. Saeed, Malak M. N. Al-Koshab et al.· 2026 6th International Confe...· 0 citations
This work investigates whether a single diffusion model trained across several imaging domains can instead serve as a prior for many CT problems simultaneously, and proposes a frozen model that out-performs analytic reconstructions in all three cases.
Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman et al.· 0 citations
