NJU and PKU team reports single-shot lensless dynamic imaging

Researchers from Nanjing University and Peking University developed a lensless imaging method that reconstructs moving scenes from a single shot with higher fidelity. The work could help shrink imaging systems for microscopy, biological monitoring, and portable sensing.

Why it matters: - Lensless imaging can make cameras and microscopes smaller, lighter, and more portable. - Dynamic scenes have been a major weakness because motion blur and mixed diffraction patterns often require multiple exposures or strong assumptions. - The new method aims to recover clearer, higher-resolution moving images from one measurement, which could matter for microscopy, live biological observation, and compact diagnostic devices.

What happened: - A joint team from Nanjing University and Peking University developed McLDI-INR, short for Mask-constraint Lensless Dynamic Imaging by Dual-Domain Collaborative Implicit Neural Representation. - The study was made available online on May 26, 2026 and published in Volume 2, Issue 2 of Intelligent Opto-Electronics on June 30, 2026. - The method combines physical modeling with deep learning for lensless imaging in dynamic scenes.

The details: - The researchers built a mask-constrained lensless dynamic imaging system that uses a binary mask to make diffraction patterns more physically interpretable. - The model feeds spatial and temporal coordinates into a neural network to learn the complex-amplitude distribution of moving objects in continuous space and time. - The framework adds a collaborative loss function across spatial and frequency domains. - A physics-model-driven frequency-domain constraint helps recover high-frequency detail and suppress motion blur and reconstruction artifacts. - Simulation tests on rapidly moving linear and nonlinear targets showed reconstructions closer to ground truth than baseline approaches. - The method better preserved contours, edge structure, and fine texture in those simulations. - Real experiments used a moving USAF resolution target and freely swimming rotifer samples. - The reconstruction improved edge sharpness on the resolution target and reduced motion blur. - In rotifers, the method captured non-rigid motion details such as tail contraction and displacement. - The paper is titled “McLDI-INR: mask-constraint lensless dynamic imaging by dual-domain collaborative implicit neural representation.” - The DOI is the paper DOI.

Between the lines: - The technical shift is not just better reconstruction; it is a move toward pairing physical optics with neural representations instead of relying mainly on hand-crafted priors. - That approach could make lensless systems more usable outside controlled lab settings because it reduces dependence on perfect models and repeated measurements. - The results also suggest lensless imaging is moving from static reconstruction toward practical dynamic sensing.

What's next: - The researchers expect the approach to support miniaturized microscopy, portable biological detection, observation of living processes, and intelligent sensing devices. - The work points to a broader design path for future lensless imaging and computational optical systems. - Funding came from the Jiangsu Association for Science and Technology Young Elite Scientists Sponsorship Program, the National Natural Science Foundation of China, and the Beijing Natural Science Foundation.