EigenCWD: a spatially-varying deconvolution algorithm for single metalens imaging

TL;DR

This paper introduces eigenCWD, a novel spatially-varying deconvolution algorithm that effectively corrects aberrations in single metalens imaging, enabling clearer images by addressing complex wavefront distortions.

Contribution

The paper presents eigenCWD, a new deconvolution method using eigendecomposition to handle spatially-varying aberrations in metalens imaging, improving image quality over traditional methods.

Findings

EigenCWD outperforms Wiener filtering in correcting spatially-varying blur.

The algorithm efficiently scales to larger images and complex blurring kernels.

EigenCWD effectively reduces aberrations like coma and astigmatism in metalens images.

Abstract

The miniaturization of optics through the use of two-dimensional metalenses has enabled novel applications in imaging. To date, single-lens imaging remains the most common configuration, in part due to the limited focusing efficiency of metalenses. This results in limitations when it comes to wavefront manipulation and, thus, unavoidable aberrations in the formed image that require computational deconvolution to deblur the image. For certain lens profiles, such as the most common hyperbolic one that results in the highest efficiencies, at large fields of view, spatially-varying aberrations such as coma or astigmatism are prominent. These aberrations cannot be corrected for by traditional deconvolution methods, such as Wiener filtering. Here, we develop a spatially-varying deconvolution algorithm based on eigenvalue column-wise decomposition (eigenCWD). EigenCWD solves a minimization problem of the error between the measured image and the estimated image of the object to be reconstructed through an approximate forward blurring model. This approximate forward model uses an eigendecomposition of the spatially-varying point spread functions for fast computation, allowing for efficient scaling to larger image sizes and blurring kernels common in metalens imaging. We demonstrate eigenCWD's ability to correct spatially-varying blur and distortions for various lens profiles, surpassing that of the Wiener filter.