Nov 2025· Journal of Physics: Photonics· Vol 8, pp. 035017· 0 citations· 44 references
PhysicsEngineering
TL;DR
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.
Abstract
Inverse problems in imaging are typically ill-posed and are solved using regularized optimization techniques or - in recent times, by employing deep neural networks. While the deep network method enables fast end-to-end reconstruction from raw measurements, it does not necessarily alter the fundamentally ill-posed nature of the underlying inverse problem. It is well known that diverse, non-redundant measurements can improve the robustness of reconstruction algorithms. However, acquiring multiple measurements typically - involves additional hardware and more complex system setups, that may not always be desirable for field deployment. In this work, we note that in both incoherent and coherent (phase retrieval) 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. A physics informed data augmentation scheme is then described where a trained network is used for generating a phase diverse pseudo-data based on a ground truth data frame, typically acquired using a standard imaging system. We validate this data augmentation approach for both incoherent and coherent optical imaging - configurations, with vortex phase as a - diversity mechanism. Specifically, we observe that the generated pseudo-data closely match the corresponding ground-truth data, with comparable noise characteristics. We further demonstrate that the true data along with the augmented pseudo-data provide- high quality inverse solutions with simpler robust reconstruction algorithms. Our results may open new avenues for leaner, high-fidelity computational imaging systems across a broad range of applications.
Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior in the limit that turns the discretized ill-conditioned problems into truly ill-posed ones, i.e., for an increasing resolution of the discretization. In this work, we review common approaches to neural operator learning in architectures that resemble a U-Net, one of the most common classical architectures for inverse imaging problems. We discuss advantages and drawbacks of the respective approaches, consider a 1D toy example for improved interpretability, and present extensive numerical experiments on how different types of neural operator U-Nets can improve a first (crude) limited angle CT-reconstruction. In particular, we study how well networks trained for a certain resolution of the discretization generalize to other resolutions. Our finding is that while U-shaped neural operator architectures are by design resolution-invariant, the classical U-Net architecture seems to be more robust with respect to resolution changes than expected.
Alexander Auras, Martin Burger, Samira Kabri et al.· 0 citations
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.
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 which standard imaging models are frequently insufficient. Recently, deep learning has emerged as an effective framework for reconstruction, since it allows the mapping from scattered measurements to object estimates to be learned directly from data. This review focuses particularly on purely data-driven methods, in which the neural network acts as the primary reconstruction engine instead of functioning as a supplementary element. The reviewed studies are examined in three dimensions: reconstruction frameworks, learning regimes, and system-level integration. Within this framework, we examine how various approaches trade off reconstruction accuracy, robustness, portability, and computational expense. We also consider training data requirements, adaptation strategies, generalization behavior, and evaluation practice. The literature shows clear progress toward more adaptive and robust reconstruction systems. However, several limitations continue to hinder broader applicability, including dependence on paired training data, fragmented out-of-distribution evaluation, the absence of standardized robustness and benchmarking protocols, and limited reporting on practical deployment. On the basis of this analysis, we identify several priorities for future research, encompassing the development of weakly supervised learning methodologies, the establishment of standardized multi-dimensional robustness assessment protocols, the advancement of modular physics-informed design strategies, and the exploration of more tightly integrated reconstruction frameworks.
Radhwan A. A. Saleh, Salah F. S. Saeed, Malak M. N. Al-Koshab et al.· 2026 6th International Confe...· 0 citations
Electromagnetic inverse scattering is a nonlinear and ill-posed computational imaging problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled training data, existing accelerated PDNN frameworks often rely on preliminary reconstruction-based region selection, which may introduce instability when the selected region is inaccurate. In this paper, a coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering. CRPDNN represents the unknown complex contrast distribution using normalized spatial coordinates and a residual convolutional network, whose parameters are optimized by enforcing consistency between the measured and model-predicted scattered fields. Unlike existing subregion-accelerated PDNN approaches, CRPDNN does not require a preliminary reconstruction, thereby avoiding dependence on its accuracy. For the reported noise-free 3-D synthetic cases, CRPDNN achieves an average relative error of 2.10\%, compared with 7.97\% for CSI and 3.99\% for $L_{2/3}$-FBE-WCIE, while providing approximately 5.5- and 12.1-fold speedups over the two baselines, respectively. Additional 2-D comparisons further demonstrate its stability and computational efficiency relative to existing PDNN frameworks. CRPDNN also maintains reliable reconstruction performance under noisy measurements, and the 3-D Fresnel experiments further indicate its potential for practical imaging applications.
Experiments on AAPM and DeepLesion under multiple sparse-view and noise settings show that CG-GLORE achieves strong quantitative performance, stable convergence, lower noise power, and improved visual fidelity compared with representative reconstruction methods.
Tran Xuan Hieu Le, D. C. Bui, V. Le et al.· 0 citations
Fast through-the-wall imaging (TWI) by inverse scattering is highly desirable for many security and civilian applications. TWI by inverse scattering requires the solution of an ill-posed and nonlinear set of equations whose solution is attained iteratively (e.g., via a distorted Born iterative method). The iterative solution requires long execution times and often does not converge, limiting the applicability of inverse scattering to real-world TWI scenarios. To address these issues, this study investigates deep learning-augmented inverse-scattering schemes that combine physics-based modeling with the learning capability of neural networks. Using images after a few distorted Born iterations as input, three neural networks, namely a traditional convolutional neural network (CNN), U-net, and mask region-based CNN (Mask R-CNN) are leveraged to obtain final high-quality images of the scatterers behind the walls and their performance is compared. Among these three techniques, the traditional CNN regresses scatterer positions and restores dielectric profiles, while U-net segments scatterers and Mask R-CNN detects scatterers. Numerical results show that U-net achieves the highest accuracy, while Mask R-CNN offers competitive accuracy and removes boundary defects, making it highly effective for TWI applications.
Abdulkadir C. Yucel, Xiaofan Jia· Balıkesir Üniversitesi Fen B...· 0 citations
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