Joint optimization of splitter pattern and image reconstruction for metasurface-based color imaging systems

Abstract

The metasurface-based splitters have demonstrated great potential in high-resolution, dark-scene, and ultra-fast imaging due to their excellent capabilities of preserving light intensity, attracting increasingly more research interests. This paper investigates the splitter pattern design, which determines the theoretical performance limit of metasurface-based color imaging systems, to guide their further development. We first establish a unified mathematical model for the splitter-based imaging process. This model reveals that the raw image detected by the splitters is degraded by noise, incomplete sampling, and blurring. Based on this, we develop a deep neural network capable of simultaneously tackling multiple image processing tasks to achieve color image reconstruction. We then conduct evaluation experiments for several existing patterns, through which we establish three design principles to guide the optimization of a splitter pattern. Furthermore, by jointly learning the parameters of our proposed imaging model and reconstruction network, we achieve an automatic optimization of a splitter pattern. The experiments demonstrate that the optimized pattern can achieve better quantitative and visual results than the original pattern, validating the effectiveness of this pattern optimization method.