Configurable multiple virtual lenses conjugated with singlet physical lens for achromatic extended depth-of-field imaging.

Abstract

An achromatic extended depth-of-field (EDOF) system can obtain clear scene information that is crucial for target recognition, dynamic monitoring, and other applications. However, the imaging performance of most optical systems is depth-variant and wavelength-variant, which leads to the generation of chromatic aberrations. Traditional optical design and image post-processing algorithms cannot effectively eliminate these chromatic aberrations. Here, we propose a deep configurable multiple virtual lenses optimization method that embeds four virtual lenses in parallel conjugated with a real lens. Combined with a lens fusion recovery network (LFRNet), it compensates for chromatic aberrations at different depths to achieve achromatic EDOF imaging. Trainable virtual optics can eliminate chromatic aberrations and overcome the limitations of traditional optics. The proposed framework reduces the optical design complexity and improves the imaging quality of a simple optical system. We validate our method using a singlet lens, and the experimental results show that the reconstructed images have an average peak signal-to-noise ratio (PSNR) improvement of 12.1447 dB and an average structural similarity index measure (SSIM) improvement of 0.2465. The proposed method opens a new avenue for ultra-compact, high-freedom, high-efficiency, and wholly configurable deep optics design, and empowers various advanced applications, such as portable photography and other complex vision tasks.