Optimal design of segmented planar imaging for dense azimuthal sampling lens array.

Area of Science:

• Optics and Photonics
• Image Reconstruction
• Signal Processing

Background:

• Traditional segmented planar imaging suffers from blurred reconstructed images due to sparse spatial frequency sampling.
• Existing methods require extensive algorithm modification for image sharpness enhancement and artifact reduction.
• Limited ideal image quality restricts optimization potential in conventional systems.

Purpose of the Study:

• To propose a novel segmented planar imager architecture for improved image quality.
• To address limitations in spatial frequency domain sampling for enhanced imaging.
• To develop a reconstruction method for continuous spatial frequency sampling.

Main Methods:

• Development of a segmented planar imager based on a dense azimuthal sampling lens array with a radial fill factor of 0.5.
• Establishment of a full-chain theoretical model for the new lens array architecture.
• Proposal of a discrete spectrum matrix reconstruction method for reduced spatial sampling point density.

Main Results:

• The dense azimuthal sampling lens array mitigates image artifacts and enhances ideal image quality.
• The discrete spectrum matrix reconstruction method achieves continuous sampling of fundamental frequencies.
• Increasing azimuthal sampling photonic integrated circuits (PICs) improves peak signal-to-noise ratio (PSNR); reducing sampling radius enhances definition.

Conclusions:

• The proposed dense azimuthal sampling lens array and reconstruction method significantly improve segmented planar imager performance.
• Optimizing the number of radial-spoke PICs and effective spatial sampling radius are crucial for system design.
• Combining structural design with advanced sampling techniques offers a significant pathway to superior imaging quality.