Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts
This work addresses the problem of enabling practical, high-quality imaging for everyday applications using ultra-thin metalens cameras, representing an incremental improvement in a domain-specific area.
The paper tackled the challenge of robust imaging with severely aberrated metalens cameras by developing an end-to-end solution combining a thin metalens with a multi-image restoration framework, achieving consistent outperformance over existing burst-mode and single-image restoration techniques in real-world handheld captures.
We tackle the challenge of robust, in-the-wild imaging using ultra-thin nanophotonic metalens cameras. Meta-lenses, composed of planar arrays of nanoscale scatterers, promise dramatic reductions in size and weight compared to conventional refractive optics. However, severe chromatic aberration, pronounced light scattering, narrow spectral bandwidth, and low light efficiency continue to limit their practical adoption. In this work, we present an end-to-end solution for in-the-wild imaging that pairs a metalens several times thinner than conventional optics with a bespoke multi-image restoration framework optimized for practical metalens cameras. Our method centers on a lightweight convolutional network paired with a memory-efficient burst fusion algorithm that adaptively corrects noise, saturation clipping, and lens-induced distortions across rapid sequences of extremely degraded metalens captures. Extensive experiments on diverse, real-world handheld captures demonstrate that our approach consistently outperforms existing burst-mode and single-image restoration techniques.These results point toward a practical route for deploying metalens-based cameras in everyday imaging applications.