Computational imaging-based single-lens imaging systems and performance evaluation.

Abstract

The minimalist optical system has a simple structure, small size, and lightweight, but the low optical complexity will produce optical aberration. Addressing the significant aberration degradation in minimalist systems, we propose a high-quality computational optical framework. This framework integrates a global point spread function (PSF) change imaging model with a transformer-based U-Net deep learning algorithm to achieve high-quality imaging in minimalist systems. Additionally, we introduce an imaging performance evaluation method based on the modulation transfer degree of resolution (MTR). We addressed severe chromatic and spherical aberrations in single-lens systems, a typical example of minimalist optical systems, by simulating the degradation process and reconstructing the imaging effects. This approach demonstrated significant improvements, thus validating the feasibility of our method. Specifically, our technique calculated the MTR values in real images captured with the GCL010109 single lens at 0.8085, and with the GCL010110 single lens at 0.8055. Our method enhanced the imaging performance of minimalist systems by 4 times, upgrading minimalist system capabilities from poor to good lens grade. This work can provide reference for wavefront coding, matelens, diffraction optical systems, and other computational imaging work. It can also promote the application of miniaturization of medical, aerospace, and head-mounted optical systems.