A Reflective Dual-Channel Optical Architecture for Compressive Computational Imaging

Off-axis reflective optical systems are widely used in the field of remote sensing due to their advantages such as long focal length, achromatic performance, and compact structure. These systems offer significant benefits for high-resolution observation. However, a fundamental trade-off between field of view (FOV) and focal length exists. Higher ground sample resolution is often pursued comes at the expense of swath width, thereby significantly reducing the system’s revisit capability. A common solution involves detector tiling to expand the FOV, but this greatly increases the system’s volume, weight, cost, power consumption, and data bandwidth, posing substantial limitations. In this paper, we propose a novel dual-channel computational imaging architecture that addresses this challenge. Leveraging advanced freeform optics, two independent optical channels are integrated to share a common focal plane. By applying compressive sensing techniques, aliased measurements captured by a single detector array can be used to computationally reconstruct the individual images from each optical path. Through the design of complementary FOVs in the two channels, the effective FOV of the off-axis reflective system is significantly expanded without substantial increases in system complexity or resource consumption. This approach offers promising potential for future high-performance remote sensing applications.