Work

Synergistic coded illumination lensless imaging via efficient acquisition and deep feature fusion

Year: 2026

Type: article

Abstract: Lensless imaging provides a compact solution for visual sensing, but its reconstruction remains an inherently ill-posed inverse problem. While coded illumination has been proven to alleviate aliasing and mitigate the ill-posedness, existing approaches are confined to pixel-domain linear reconstruction, lacking deep exploration of the latent structural correlations among multiple measurements and failing to fully exploit their complementary information. This work presents a framework that integrates multi-pattern complementary coded illumination with deep feature-domain exploration. Guided by the frequency-domain correlations of the coded scenes, we design a feature decoupling and fusion network that disentangles distinct features for parallel extraction and fusion, ensuring high-fidelity recovery. Experiments demonstrate that our framework achieves superior structural and textural preservation with reduced measurement overhead, offering an efficient and robust solution for lensless imaging.

Source: Optics & Laser Technology

DOI: 10.1016/j.optlastec.2026.115168

Authors: Tianjiao Zeng, Zhenxiao Zhou, Xu Zhan, Yuwei Liu, Xiaoling Zhang

Institution: University of Electronic Science and Technology of China

Language: English

Topic: Random lasers and scattering media

Subfield: Acoustics and Ultrasonics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,230

Funders: National Natural Science Foundation of China, Sichuan Province Science and Technology Support Program