Meta-optics for advanced computational imaging

Minsung Kang

1,2,#

Myung Chan Sung

1,#

Trevon Badloe

1,2,3,*

*Correspondence to: Trevon Badloe, Department of Electronics and Information Engineering, Korea University, Sejong 30019, South Korea; Division of Smart Energy Convergence Engineering, Korea University, Sejong 30019, South Korea; Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, South Korea. E-mail: trevon@korea.ac.kr

Light Manip Appl. 2026;1:202621. 10.70401/lma.2026.0023

Received: August 19, 2026Accepted: November 12, 2026Published: November 13, 2026

This manuscript is made available in its unedited form to allow early access to the reported findings. Further editing will be completed before final publication. As such, the content may include errors, and standard legal disclaimers are applicable.

Abstract

Computational imaging fundamentally transforms conventional optical capture by combining physical light encoding with digital reconstruction algorithms. While physics-based models initially enabled functional inversion, the integration of data-driven artificial intelligence has significantly expanded reconstruction quality and feature extraction capabilities. However, deploying complex deep learning models on edge platforms introduces severe computational bottlenecks, while conventional multielement refractive optics inherently constrain physical miniaturization and high-dimensional light control. Meta-optics offers a promising hardware platform to address these physical and computational limits. By utilizing planar arrays of subwavelength nanostructures, metasurfaces modulate amplitude, phase, polarization, and orbital angular momentum within an ultra-thin footprint, enabling fast, highly parallelized optical pre-processing. This capability allows metasurfaces to execute complex spatial and spectral transformations in the optical domain, reducing the backend computational load, while digital algorithms compensate for single layer optical aberrations. Moving beyond independent component optimization, the field is evolving toward end-to-end co-design, where nanostructure parameters and neural network weights are jointly optimized through differentiable programming frameworks. This review traces the development of meta-optics-based computational imaging and outlines key directions for compact, multifunctional sensing architectures.

Keywords

Metasurfaces, computational imaging, analog processing, computational optics, opto-electronic co-design, multifunctional imaging

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