Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network

Xiao Peng Jin, An Dong Xiong, Fan Liu, Xiao Qing Wang, Wei Zhang, Chang Heng Li, Xu Ri Yao*, Xue Feng Liu*, Qing Zhao*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Parallel compressive super-resolution imaging has attracted increasing attention in recent years. However, the super-resolution quality depends on modulation masks and reconstruction algorithms. A deep-learning method provides an efficient solution, but in wide field-of-view (FOV) scenarios, the differences between optical transfer functions (OTFs) of each pixel increase the system complexity and limit its practical application. This study proposed a wide FOV parallel compressive super-resolution imaging approach based on a physics-enhanced network. First, the network and modulation masks of an arbitrary 128 × 128-pixel region were trained; then, the trained network was fine-tuned for the rest of the 128 × 128-pixel regions in the entire wide FOV, which effectively eliminated the OTF variability. Numerical simulations and practical experiments demonstrated that through the proposed approach, super-resolution images of 1020 × 1500 pixels can be reconstructed from 272 × 400-pixel low-resolution measurements using only three designed masks, with the resolution enhanced 3.75 × 3.75 times and the peak signal-to-noise ratio improved by 89.4% compared to the results of the previous compressed sensing algorithm. Besides, the training time was dramatically reduced by 95.5-fold compared with the traditional training strategy for each region alone. This approach decreases the imaging complexity of wide FOV and achieves the high-quality super-resolution reconstruction under few trained masks, thus we believe it can promote rapid imaging for super-resolution and a wide FOV ranging from infrared to terahertz.

源语言英语
文章编号211106
期刊Applied Physics Letters
124
21
DOI
出版状态已出版 - 20 5月 2024

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