Bowen Wang

Fourier ptychography (FP) enables synthetic aperture imaging beyond the coherent diffraction limit of the imaging system, yet its extension to long-range scenarios remains fundamentally hindered by atmospheric turbulence. Turbulence introduces stochastic, time-varying wavefront distortions that violate both the deterministic pupil model and the coherent spectral overlapping required by FP, rendering conventional reconstruction strategies physically inconsistent. Here, we propose turbulence-resilient macroscopic Fourier ptychography (TRMFP), a unified computational framework that reformulates FP as a statistically consistent imaging process through random media. TRMFP integrates a speckle-interferometry-inspired preprocessing stage with a closed-loop ptychographic reconstruction. By exploiting ensemble statistics from multiple short-exposure measurements at each aperture position, TRMFP recovers turbulence-suppressed, diffraction-limited sub-aperture images, followed by joint optimization of the object and spatially varying wavefront aberrations under low-rank and total-variation constraints. Simulations and experiments validate the robustness of TRMFP across a wide range of turbulence strengths ( D / r ₀ =1.5 to 7.5), achieving ∼2× resolution improvement in 1.5 m phase-screen experiments and ∼1.6× gain under 5 m dynamic turbulence over conventional FP. Without requiring additional hardware, wavefront sensors, or training data, TRMFP establishes a physically grounded and computationally efficient solution for super-resolution imaging through atmospheric turbulence, bridging the gap between Fourier ptychography and real-world long-range synthetic aperture imaging applications.

High‐resolution imaging of distant, dynamic targets is fundamentally limited by finite‐aperture diffraction. Unlike radar, optical detectors record only intensity and lack the temporal resolution for direct phase measurement, hindering straightforward extension of synthetic aperture techniques to the optical domain. While Macroscopic Fourier ptychography (FP) offers a promising computational alternative, its reliance on mechanical scanning or engineered illumination restricts its use to cooperative, static scenes. In this paper, we present Inverse Synthetic Aperture Macroscopic Fourier ptychography (ISAMFP), which, to the best of our knowledge, is the first practical realization of inverse synthetic aperture imaging for diffusely reflecting targets within the Fourier ptychography framework. By transforming the natural target motion from a detrimental factor into an effective source of angular diversity, ISAMFP eliminates the need for mechanical scanning or illumination modulation and achieves resolution enhancement far beyond the aperture limit using intensity‐only measurements. Experimental validations on USAF resolution targets, unmanned aerial vehicles, and complex multi‐target scenes highlight both the robustness and versatility of ISAMFP, establishing it as a powerful tool for non‐interferometric synthetic aperture imaging of moving, non‐cooperative targets, with broad implications for surveillance, defense, and remote sensing.

A recent study employing high-spatial-resolution photoemission electron microscopy (PEEM) achieved, for the first time, weak-disturbance imaging of the ultra-confined nanoslit mode in a coupled nanowire pair (CNP), revealing its quasi-three-dimensional field distribution.

Optical imaging has long been dominated by traditional lens-based systems that, despite their success, are inherently limited by size, weight, and cost. Lensless imaging seeks to overcome these limitations by replacing lenses with thinner, lighter, and cheaper optical modulators and reconstructing images computationally, while facing trade-offs in image quality, artifacts, and flexibility inherent in traditional static modulation. Here, we propose a lensless imaging method with programmable Fresnel zone aperture (FZA), termed LIP. With a commercial liquid crystal display, we designed an integrated LIP module and demonstrated its capability of high-quality artifact-free reconstruction through dynamic modulation and offset-FZA parallel merging. Compared to static-modulation approaches, LIP achieves a 2.5× resolution enhancement and a 3 decibels improvement in signal-to-noise ratio in “static mode” while maintaining an interaction frame rate of 15 frames per second in “dynamic mode.” Experimental results demonstrate LIP’s potential as a miniaturized platform for versatile advanced imaging tasks like virtual reality and human-computer interaction.

Non-interferometric synthetic aperture imaging (SAI) shows significant potential in Earth observation, astronomy, and remote sensing. However, these methods often involve time-consuming processes for wave field acquisition and iterative image reconstruction. In this Letter, we present a non-iterative far-field synthetic aperture imaging method, macroscopic space-domain Kramers–Kronig relations synthetic aperture imaging (MSKR-SAI). Unlike traditional macroscopic Fourier ptychography (FP), MSKR-SAI bypasses redundant iterations and requirements for highly overlapping images, reducing reconstruction time from 4.97 s to 0.17 s—a 26-fold speedup. By utilizing only six sub-aperture intensity images, MSKR-SAI reconstructs complex amplitude information and synthesizes the aperture in a fully determinist manner. Simulations and experimental results show a twofold resolution improvement with accurate detail recovery and minimal artifacts. Furthermore, MSKR-SAI maintains robustness even when the Kramers–Kronig relations are not strictly met. The combination of non-iterative reconstruction, noise resilience, and computational efficiency positions MSKR-SAI as a promising method for high-resolution, artifact-free far-field imaging.

To reveal the fundamental aspects hidden behind a variety of transient events in mechanics, physics, and biology, the highly desired ability to acquire three-dimensional (3D) images with ultrafast temporal resolution has been long sought. As one of the most commonly employed 3D sensing techniques, fringe projection profilometry (FPP) reconstructs the depth of a scene from stereo images taken with sequentially structured illuminations. However, the imaging speed of current FPP methods is generally capped at several kHz, which is limited by the projector-camera hardware and the number of fringe patterns required for phase retrieval and unwrapping. Here we report a novel learning-based ultrafast 3D imaging technique, termed single-shot super-resolved FPP (SSSR-FPP), which enables ultrafast 3D imaging at 100,000 Hz. SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. To demonstrate the high spatio-temporal resolution of SSSR-FPP, we present 3D videography of several transient scenes, including rotating turbofan blades, exploding building blocks, and the reciprocating motion of a steam engine, etc., which were previously challenging or even impossible to capture with conventional methods. Experimental results establish SSSR-FPP as a significant step forward in the field of 3D optical sensing, offering new insights into a broad spectrum of dynamic processes across various scientific disciplines.

We propose a novel computational super-resolution imaging technique, coded aperture super-resolution imaging based on a compound eye camera array, which is to reverse the aliasing information by introducing four different coded modulations.