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Forward and backward scattering provide complementary volumetric and interfacial information, yet conventional three-dimensional (3D) imaging typically accesses only one. We present a substrate-enhanced diffraction tomography approach that simultaneously recovers both channels under multi-angle epi-illumination. This geometry captures one forward- and two backward-scattering bands in axially symmetric Fourier regions, where their complementary coverage enables phase–absorption separation in a non-Hermitian spectrum. Explicit 3D transfer functions are derived for both channels, and an axial Kramers–Kronig relation is established to incorporate substrate-induced boundary conditions in a unified framework. Our results establish a label-free, high-resolution 3D imaging modality that surpasses the limits of existing methods. 

We investigate a convolutional neural network with the first layer implemented by specially designed plasmonic metasurface photodetectors, showing an order of magnitude decrease in computational complexity for the accurate classification of transparent biological cells. 

We introduce a reflection-mode diffraction tomography technique that enables the simultaneous recovery of forward- and backward-scattering information for high-resolution 3D refractive index reconstruction. Our technique works by imaging a sample on a highly reflective substrate and employing a multiple-scattering model and a reconstruction algorithm. It combines the modified Born series as the forward model, Bloch and perfect electric conductor boundary conditions to handle oblique incidence and substrate reflections, and the adjoint method for efficient gradient computation in solving the inverse-scattering problem. We validate the technique through simulations and experiments, achieving accurate reconstructions in samples with high refractive index contrasts and complex geometries. Forward scattering captures smooth axial features, while backward scattering reveals complementary interfacial details. Experimental results on dual-layer resolution targets, 3D randomly distributed beads, phase structures obscured by highly scattering fibers, fixed breast cancer cells, and fixedC. elegansdemonstrate its robustness and versatility. This technique holds promise for applications in semiconductor metrology and biomedical imaging. 

We propose a novel algorithm based on the split-step non-paraxial model for different intensity diffraction tomography setups to recover the 3D refractive index distribution of multiple-scattering biological samples. 

The development of photonic technologies for machine learning is a promising avenue toward reducing the computational cost of image classification tasks. Here we investigate a convolutional neural network (CNN) where the first layer is replaced by an image sensor array consisting of recently developed angle-sensitive metasurface photodetectors. This array can visualize transparent phase objects directly by recording multiple anisotropic edge-enhanced images, analogous to the feature maps computed by the first convolutional layer of a CNN. The resulting classification performance is evaluated for a realistic task (the identification of transparent cancer cells from seven different lines) through computational-imaging simulations based on the measured angular characteristics of prototype devices. Our results show that this hybrid optoelectronic network can provide accurate classification (>90%) similar to its fully digital baseline CNN but with an order-of-magnitude reduction in the number of calculations. 

We report a new technique for single-shot quantitative phase retrieval from transparent objects, based on plasmonic metasurface photodetectors featuring an asymmetric angular response around normal incidence combined with a particularly simple optical setup.