The recent surge in data-intensive applications and increasing demands for improved latency and energy efficiency have led to the reemergence of optical computing as a vital complement to traditional electronic processors. Diffractive deep neural networks (D²NNs) represent the cutting edge of this renaissance, utilizing light propagation physics through engineered diffractive layers to perform complex computations in the photonic domain. As an inherently parallel, high-bandwidth, and energy-efficient platform, D²NNs facilitate tasks including lensless imaging, compressive sensing, and phase retrieval at speeds surpassing current GPUs and ASICs. These networks optimize learnable phase and amplitude masks across multiple diffractive planes through end-to-end, gradient-based training, incorporating task-specific priors directly into the hardware architecture, thereby enhancing resilience against noise and optical aberrations. The significance of D²NN-based computational imaging (CI) stems from its ability to transfer computational burden from electronic systems to passive photonic structures and its potential to enable novel imaging applications in biomedical diagnostics, remote sensing, and industrial inspection. With advancing fabrication techniques in metasurfaces and nano-printing, D²NNs are positioned to become fundamental to next-generation intelligent photonic systems that integrate sensing, computation, and decision-making processes at light speed.

CI systems based on D²NN technology incorporate high-dimensional matrix operations and feature extraction directly into light propagation processes. The system architecture consists of cascaded diffractive layers, each implemented as a learnable phase mask that modulates the incident wavefront according to established diffraction models. Through comprehensive simulation-based training, the optimized phase configuration enables various imaging capabilities during light transmission, including holographic display, phase imaging, and super-resolution imaging. This integrated approach achieves processing speeds at the picosecond scale while maintaining power consumption at the microwatt level.

The structure and operational principles of optical diffraction neural networks are initially presented in Fig. 1, encompassing (1) the fundamental D²NN architecture, (2) the modulation principle achieved through diffractive optical elements, and (3) the modulation process implemented via metasurfaces. D²NN comprises multiple cascaded, grid-patterned diffractive layers: phase control is attained by modifying each pixel’s transmittance in the diffractive elements, while metasurfaces enable more sophisticated manipulation of the optical field, including its polarization, amplitude, and phase. Figure 2 presents a comparative analysis of two optical paths: one for computational holographic wavefront reconstruction using spatial light modulators, and another for optical information detection based on a D²NN. In Fig. 3, the relationship between the holographic display process and D²NN is examined, addressing speckle and noise suppression in holographic reconstructions and the elimination of artifacts from holograms. Figure 4 illustrates network-structure enhancements of D²NN for holographic display, including Res-D²NN with residual skip-connections, multi-view D²NN arrays, and pyramid-shaped D²NN. Phase-imaging investigations appear in Fig. 5 and Fig. 6: Fig. 5 describes a diffractive phase imager, a multispectral phase imager, and a 3D multi-plane wavelength-multiplexed phase imager; Fig. 6 presents a hybrid multiplexing design for all-optical complex-amplitude imaging and an all-optical phase imaging scheme through random scattering media. Super-resolution imaging via D²NN is explained in Fig. 7, including a solid-immersion subwavelength amplitude and phase imager and the 3D optimized optical-field principle of an optical super-oscillation D²NN. Figures 8 and 9 address all-optical scattering objects and single-pixel imaging, respectively: Fig. 8 elaborates on all-optical scattering imaging, a highly robust “vaccination” training-strategy network, a hybrid information-transmission system combining electronic encoding with diffractive decoding, and fiber-integrated D²NN; Fig. 9 includes task-specific image reconstruction, all-optical object hiding and defect detection, and broadband single-pixel diffraction networks. Finally, the review examines the field’s outstanding challenges and current research directions, including the absence of strong optical nonlinearities, challenges in adapting to dynamic scenes, and discrepancies between simulated physics and real-world propagation.

D²NN has emerged as an increasingly significant tool for computational imaging. In summary, comprehensive and detailed research remains necessary to advance holographic display, phase imaging, super-resolution imaging, scattering imaging, and single-pixel imaging with D²NN, to facilitate the academic and engineering development of this imaging paradigm.