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.

In this Letter, we report a new long-range synthetic aperture Fourier ptychographic imaging technique, termed learning-based single-shot synthetic aperture imaging (LSS-SAI). LSS-SAI uses a camera array to record low-resolution intensity images corresponding to different non-overlapping spectral regions in parallel, which are synthesized to reconstruct a super-resolved high-quality image based on a physical model-based dual-regression deep neural network. Compared with conventional macroscopic Fourier ptychographic imaging, LSS-SAI overcomes the stringent requirement on a large amount of raw data with a high spectral overlapping ratio for high-resolution, high signal-to-noise imaging of reflective objects with diffuse surfaces, making single-shot long-range synthetic aperture imaging possible. Experimental results on rough reflective samples show that our approach can improve the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 10.56 dB and 0.26, respectively. We also demonstrate the single-shot ptychography capability of the proposed approach by the synthetic aperture imaging of a dynamic scene at a camera-limited speed (30 fps). To the best of our knowledge, this is the first demonstration of macroscopic Fourier ptychography to single-shot synthetic aperture imaging of dynamic events.

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.

Fourier ptychography (FP) technique is a promising super‐resolution tool in noninterferometric synthetic aperture research thanks to its unique capabilities for circumventing the physical limitation of space bandwidth product. However, FP imaging in the long‐range scenario suffers from field‐of‐view (FOV) attenuation due to spherical‐wave imaging characteristics, and the speckle noise of most targets further restricts the FP's application in far‐field detection. Herein, a remote FP (R‐FP) technique is proposed to address the issues by combining the idea of quasi‐plane wave illumination and the total variation‐guided filtering (TVGF) method. Compared with conventional macroscopic Fourier ptychographic imaging, R‐FP overcomes the contradiction between FOV and detection range and mitigates the effect of speckle noise by utilizing the TVGF method, as demonstrated by high‐resolution imaging of various targets (including United States Air Force (USAF) resolution chart, spade poker, and fingerprint). In particular, the long‐range FP capability of the proposed approach is demonstrated by the synthetic aperture imaging of a King poker card at 12 m away. To the best of the authors’ knowledge, R‐FP achieves the farthest detection range of macroscopic Fourier ptychographic imaging up to date, demonstrating its potential for application in far‐field detection.