  Achieving both high-performance and wide field-of-view (FOV) super-resolution
imaging has been attracting increasing attention in recent years. However, such
goal suffers from long reconstruction time and huge storage space. Parallel
compressive imaging (PCI) provides an efficient solution, but the
super-resolution quality and imaging speed are strongly dependent on precise
optical transfer function (OTF), modulation masks and reconstruction algorithm.
In this work, we propose a wide FOV parallel compressive super-resolution
imaging approach based on physics enhanced network. By training the network
with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the
network with other OTFs within rest regions of FOV, we realize both mask
optimization and super-resolution imaging with up to 1020x1500 wide FOV.
Numerical simulations and practical experiments demonstrate the effectiveness
and superiority of the proposed approach. We achieve high-quality
reconstruction with 4x4 times super-resolution enhancement using only three
designed masks to reach real-time imaging speed. The proposed approach promotes
the technology of rapid imaging for super-resolution and wide FOV, ranging from
infrared to Terahertz.
